CSC 516 AI Exam 1

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Tasmania

the island south of Australia

directed arc consistency

A CSP is defined to be directed arc-consistent under an ordering of variables X1,X2, . . . ,Xn if and only if every Xi is arc-consistent with each Xj for j > i.

constraint binary

A binary constraint relates two variables.

constraint global

A constraint involving an arbitrary number of variables

positive literal

A literal is either an atomic sentence (a positive literal)

inference

A logical interpretation based on prior knowledge and experience. an algorithm can search (choose a new variable assignment from several possibilities) or do a specific type of inference

prediction

A logical statement about what will happen if the hypothesis is correct. simply generating a new belief state

commutativity (in search problems)

A problem is commutative COMMUTATIVITY if the order of application of any given set of actions has no effect on the outcome. CSPs are commutative because when assigning values to variables, we reach the same partial assignment regardless of order. Therefore, we need only consider a single variable at each node in the search tree.

constraint satisfaction problem (CSP)

A problem is solved when each variable has a value that satisfies all the constraints on the variable

relaxed problem

A problem with fewer restrictions on the actions.

leaf node

A tree node that has no children. Their expansion causes the frontier

response

An action or change in behavior that occurs as a result of a stimulus.

rollout

An alternative is to do Monte Carlo simulation to evaluate a position. Start with an alpha-beta (or other) search algorithm. From a start position, have the algorithm play thousands of games against itself, using random dice rolls. In the case of backgammon, the resulting win percentage has been shown to be a good approximation of the value of the position, even if the algorithm has an imperfect heuristic and is searching only a few plies (Tesauro, 1995). For games with dice, this type of simulation is called a rollout.

consistency

An assignment that does not violate any constraint A second stronger condition of the heuristic. States that h(n) ≤ c(n, a, n) + h(n'), where: n = an arbitrary node n' = successor node c(n, a, n') = cost of an action from n to success n' AKA, monotonicity, which in functional analysis, means the heuristic function is never not increasing or never not decreasing

environment properties

An environment can be described as a situation in which an agent is present. The environment is where agent lives, operate and provide the agent with something to sense and act upon it. An environment is mostly said to be non-feministic.

environment partially observable

An environment might be partially observable because of noisy and inaccurate sensors or because parts of the state are simply missing from the sensor data—for example, a vacuum agent with only a local dirt sensor cannot tell whether there is dirt in other squares, and an automated taxi cannot see what other drivers are thinking.

environment one-shot

An extreme form of transfer learning aims to train a new network by showing it just a handful of examples, and sometimes only one. Known as one-shot or few-shot learning

soundness (of inference)

An inference algorithm that derives only entailed sentences is called sound or truth preserving. Soundness is a highly desirable property. An unsound inference procedure essentially makes things up as it goes along—it announces the discovery of nonexistent needles. It is easy to see that model checking, when it is applicable, is a sound procedure.

logical inference

An inference based on background knowledge and facts from a story or text;not a "wild" guess

problem optimization constrained

An optimization problem is constrained if solutions must satisfy some hard constraints on the values of the variables.

CLP

Constraint logic programming

continuous domains

Constraint satisfaction problems with continuous domains are common in the real world and are widely studied in the field of operations research. For example, the scheduling of experiments on the Hubble Space Telescope requires very precise timing of observations; the start and finish of each observation and maneuver are continuous-valued variables that must obey a variety of astronomical, precedence, and power constraints. The best-known category of continuous-domain CSPs is that of linear programming problems, where constraints must be linear equalities or inequalities.

metalevel state space

Each state in a metalevel state space captures the internal (computational) state of a program that is searching in an object-level state space such as Romania. For example, the internal state of the A∗ algorithm consists of the current search tree. Each action in the metalevel state space is a computation step that alters the internal state; for example, each computation step in A∗ expands a leaf node and adds its successors to the tree.

state space metalevel

optimal control theory

Finding optimal solutions in continuous spaces is the subject matter of several fields, including optimization theory, optimal control theory, and the calculus of variations. The basic techniques are explained well by Bishop (1995); Press et al. (2007) cover a wide range of algorithms and provide working software.

hill climbing first-choice

First-choice hill climbing implements stochastic hill climbing by generating successors randomly until one is generated that is better than the current state. This is a good strategy when a state has many (e.g., thousands) of successors.

value symmetry

For every consistent solution, there is actually a set of n! solutions formed by permuting the color names. For example, on the Australia map we know that WA,NT, and SA must all have different colors, but there are 3! = 6 ways to assign the three colors to these three regions. This is called value symmetry. We would like to reduce the search space by a factor of n! by breaking the symmetry.

Horn form

Forward chaining and backward chaining are very natural reasoning algorithms for knowledge bases in Horn form.

genetic programming

Genetic programming is the process of enhancing computer programs using algorithms inspired by biological evolution. Programming languages that lend themselves naturally to genetic programming are those able to evaluate their own code natively.

learning blocks-world

Hebb's learning methods were enhanced by Bernie Widrow (Widrow and Hoff, 1960; Widrow, 1962), who called his networks adalines, and by Frank Rosenblatt (1962) with his perceptrons. The perceptron convergence theorem (Block et al., 1962) says that the learning algorithm can adjust the connection strengths of a perceptron to match any input data, provided such a match exists.

theorem prover

Herbert Gelernter (1959) constructed the Geometry Theorem Prover, which was able to prove theorems that many students of mathematics would find quite tricky.

DEEP BLUE ix

IBM's DEEP BLUE became the first computer program to defeat the world champion in a chess match when it bested Garry Kasparov by a score of 3.5 to 2.5 in an exhibition match (Goodman and Keene, 1997). Kasparov said that he felt a "new kind of intelligence" across the board from him. Newsweek magazine described the match as "The brain's last stand." The value of IBM's stock increased by $18 billion. Human champions studied Kasparov's loss and were able to draw a few matches in subsequent years, but the most recent human-computer matches have been won convincingly by the computer.

satisfaction (in logic)

If a sentence α is true in model m, we say that m satisfies α or sometimes m is a model of α. We use the notation M(α) to mean the set of all models of α.

minimum global

If elevation corresponds to cost, then the aim is to find the lowest valley

Modus Ponens

If p, then q. p. Therefore, q. (Valid)

environment unobservable

If the agent has no sensors at all then the environment

environment semidynamic

If the environment itself does not change with the passage of time but the agent's performance score does,

environment deterministic

If the next state of the environment is completely determined by the current state and the action executed by the agent, then we say the environment is deterministic

environment stochastic

If the next state of the environment is completely determined by the current state and the action executed by the agent, then we say the environment is deterministic; otherwise, it is stochastic.

implication

Implications are also known as rules or if-then statements. The implication symbol is sometimes written in other books as ⊃ or →.

theorem proving

In 1963, McCarthy started the AI lab at Stanford. His plan to use logic to build the ultimate Advice Taker was advanced by J. A. Robinson's discovery in 1965 of the resolution method (a complete theorem-proving algorithm for first-order logic

pruning

In AI, any particular nodes not explored in A because its f(n) is larger than the C (the cost of getting to the successor)

head (of Horn clause)

In Horn form, the premise is called the body and the conclusion is called the head.

problem assembly sequencing

In assembly problems, the aim is to find an order in which to assemble the parts of some object. If the wrong order is chosen, there will be no way to add some part later in the sequence without undoing some of the work already done. Checking a step in the sequence for feasibility is a difficult geometrical search problem closely related to robot navigation. Thus, the generation of legal actions is the expensive part of assembly sequencing. Any practical algorithm must avoid exploring all but a tiny fraction of the state space.

environment sequential

In sequential environments, on the other hand, the current decision could affect all future decisions. Chess and taxi driving are sequential: in both cases, short-term actions can have long-term consequences. Episodic environments are much simpler than sequential environments because the agent does not need to think ahead.

degree heuristic

It attempts to reduce the branching factor on future choices by selecting the variable that is involved in the largest number of constraints on other unassigned variables.

MA* search

It seems sensible, therefore, to use all available memory. Two algorithms that do this are MA∗ (memory-bounded A∗) and SMA∗ (simplified MA∗). SMA∗ is—well—simpler, so we will describe it.

topological sort

Linear ordering of the vertices of a directed graph such that for every directed edge "uv" which connects "u" to "v" (u points to v), u comes before v. This ordering is only possible if and only if there are no directed cycles in the graph, therefore, it must be a DAG.

game multiplayer

Many popular games allow more than two players. Let us examine how to extend the minimax idea to multiplayer games. This is straightforward from the technical viewpoint, but raises some interesting new conceptual issues.

model checking

Mathematical proof of correctness. Exponential time and space worst case complexity. The inference algorithm illustrated in Figure 7.5 is called model checking, because it enumerates all possible models to check that α is true in all models in which KB is true, that is, that M(KB) ⊆ M(α).

problem n queens

Min-conflicts is surprisingly effective for many CSPs. Amazingly, on the n-queens problem, if you don't count the initial placement of queens, the run time of min-conflicts is roughly independent of problem size. It solves even the million-queens problem in an average of 50 steps (after the initial assignment). This remarkable observation was the stimulus leading to a great deal of research in the 1990s on local search and the distinction between easy and hard problems,

Monte Carlo simulation

MiniMax instead of going through every possibility, sample only a handful of terminal nodes As opposed to that, it could mean run a lot of random games against yourself and see which moves lead to desired outcomes

MST

Minimum spanning tree Least weight that connects all nodes No cycles

frame in representation

Minsky's idea of frames (1975), adopted a more structured approach, assembling facts about particular object and event types and arranging the types into a large taxonomic hierarchy analogous to a biological taxonomy.

logic nonmonotonic

Nonmonotonic logics, which violate the monotonicity property, capture a common property of human reasoning: changing one's mind. This kind of reasoning is said to exhibit nonmonotonicity, because the set of beliefs does not grow monotonically over time as new evidence arrives. Nonmonotonic logics have been devised with modified notions of truth and entailment in order to capture such behavior. We will look at two such logics that have been studied extensively: circumscription and default logic.

distribute ∨ over ∧

Now we have a sentence containing nested ∧ and ∨ operators applied to literals. We apply the distributivity law from Figure 7.11, distributing ∨ over ∧ wherever possible. (¬B1,1 ∨ P1,2 ∨ P2,1) ∧ (¬P1,2 ∨ B1,1) ∧ (¬P2,1 ∨ B1,1) .

least-constraining-value heuristic

Once a variable has been selected, the algorithm must decide on the order in which to examine its values. For this, the least-constraining-value heuristic can be effective in some cases. It prefers the value that rules out the fewest choices for the neighboring variables in the constraint graph.

heuristic least-constraining-value

game Othello

Othello, also called Reversi, is probably more popular as a computer game than as a board game. It has a smaller search space than chess, usually 5 to 15 legal moves, but evaluation expertise had to be developed from scratch. In 1997, the LOGISTELLO program (Buro, 2002) defeated the human world champion, TakeshiMurakami, by six games to none. It is generally acknowledged that humans are no match for computers at Othello.

logic propositional inference

Our goal now is to decide whether KB |= α for some sentence α. For example, is ¬P1,2 entailed by our KB? Our first algorithm for inference is a model-checking approach that is a direct implementation of the definition of entailment: enumerate the models, and check that α is true in every model in which KB is true. Models are assignments of true or false to every proposition symbol. Returning to our wumpus-world example, the relevant proposition symbols are B1,1, B2,1, P1,1, P1,2, P2,1, P2,2, and P3,1. With seven symbols, there are 27 =128 possible models; in three of these, KB is true (Figure 7.9). In those three models, ¬P1,2 is true, hence there is no pit in [1,2]. On the other hand, P2,2 is true in two of the three models and false in one, so we cannot yet tell whether there is a pit in [2,2].

PEAS description

Performance Criteria: how to evaluate how the agent behaves Environment: everything that the agent perceives or acts upon Actuators: components that the agent has to act upon the environment Sensors: components that the agent has to sense the environment

problem constraint optimization

Preference constraints can often be encoded as costs on individual variable assignments—for example, assigning an afternoon slot for Prof. R costs 2 points against the overall objective function, whereas a morning slot costs 1. With this formulation, CSPs with preferences can be solved with optimization search methods, either path-based or local. We call such a problem a constraint optimization problem, or COP. Linear programming problems do this kind of optimization.

robot navigation

Robot navigation is a generalization of the route-finding problem described earlier. Rather than following a discrete set of routes, a robot can move in a continuous space with (in principle) an infinite set of possible actions and states. For a circular robot moving on a flat surface, the space is essentially two-dimensional. When the robot has arms and legs or wheels that must also be controlled, the search space becomes many-dimensional. Advanced techniques are required just to make the search space finite.

BACKTRACK

See pic

BACKTRACKING-SEARCH

See pic

implicative normal form

Show that every clause (regardless of the number of positive literals) can be written in the form (P1 ∧ · · · ∧ Pm) ⇒ (Q1 ∨ · · · ∨ Qn), where the Ps and Qs are proposition symbols. A knowledge base consisting of such sentences is in implicative normal form or Kowalski form (Kowalski, 1979).

knowledge acquisition

Storage of information in long-term memory

human-level AI

That which strives for "machines that think, that learn and that create." First at Minsky's symposium in 2004.

SATMC

The SATMC satisfiability checker was used to detect a previously unknown vulnerability in a Web browser user sign-on protocol (Armando et al., 2008).

syntax

The arrangement of words and phrases to create well-formed sentences in a language.

proposition symbol

The atomic sentences consist of a single proposition symbol. Each such symbol stands for a proposition that can be true or false. We use symbols that start with an uppercase letter and may contain other letters or subscripts

killer move

The best moves are often called killer moves and to try them first is called the killer move heuristic. moves that are explored first in alpha-beta pruning so as to make the algorithm more efficient

brain computational power

The brain makes up for that with far more storage and interconnection than even a high-end personal computer, although the largest supercomputers have a capacity that is similar to the brain's. (It should be noted, however, that the brain does not seem to use all of its neurons simultaneously.)

case statement (in condition plans)

The break ( switch statement) statement ends the case statement

space complexity

The concept of how much space an algorithm requires. One of the 4 measures of problem-solving performance: how much memory is needed to perform the search?

grounding

The final issue to consider is grounding—the connection between logical reasoning processes and the real environment in which the agent exists. In particular, how do we know that KB is true in the real world? (After all, KB is just "syntax" inside the agent's head.)

heuristic path algorithm

The heuristic path algorithm (Pohl, 1977) is a best-first search in which the evaluation function is f(n) = (2 − w)g(n) + wh(n).

laws of thought

The law of identity, the law of non-contradiction, the law of excluded middle. the fundamental assumptions on which logic is based

belief state

The set of all logically possible board states given the complete history of percepts to date. representing the agent's current belief about the possible physical states it might be in, given the sequence of actions and percepts up to that point.

tree width

The tree width of a tree decomposition of a graph is one less than the size of the largest subproblem; the tree width of the graph itself is defined to be the minimum tree width among all its tree decompositions.

agent reflex

These agents select actions on the basis AGENT of the current percept, ignoring the rest of the percept history

observation sentences

This doctrine holds that all knowledge can be characterized by logical theories connected, ultimately, to observation sentences that correspond to sensory inputs; thus logical positivism combines rationalism and empiricism.

unit clause

Thus, the unit resolution rule takes a clause—a disjunction of literals—and a literal and produces a new clause. Note that a single literal can be viewed as a disjunction of one literal, also known as a unit clause.

conflict set

To do this, we will keep track of a set of assignments that are in conflict with some value for SA

straight-line distance

To measure line distance between two points

landscape (in state space)

To understand local search, we find it useful to consider the state-space landscape (as in Figure 4.1). A landscape has both "location" (defined by the state) and "elevation" (defined by the value of the heuristic cost function or objective function).

ANALOGY

Tom Evans's ANALOGY program (1968) solved geometric analogy problems that appear in IQ tests. Daniel Bobrow's STUDENT program (1967) solved algebra story problems, such as the following: If the number of customers Tom gets is twice the square of 20 percent of the number of advertisements he runs, and the number of advertisements he runs is 45, what is the number of customers Tom gets?

AI Winter

Work like this suggests that the "knowledge bottleneck" in AI—the problem of how to express all the knowledge that a system needs—may be solved in many applications by learning methods rather than hand-coded knowledge engineering, provided the learning algorithms have enough data to go on (Halevy et al., 2009). Reporters have noticed the surge of new applications and have written that "AI Winter" may be yielding to a new Spring (Havenstein, 2005). As Kurzweil (2005) writes, "today, many thousands of AI applications are deeply embedded in the infrastructure of every industry."

queue

a data structure for holding the frontier

fitness landscape

a heuristic representation of fitness as a function of genotype or phenotype

admissible heuristic

a heuristic which doesn't overestimate the actual path cost Allows the A* algorithm to be optimal

explored set

a list of all of the already explored nodes in order to prevent redundant paths AKA, a closed list

rationality perfect

achieving perfect rationality—always doing the right thing—is not feasible in complicated environments. The computational demands are just too high. The working hypothesis that perfect rationality is a good starting point for analysis.

fuzzy logic

allows a smooth, gradual transition between human and computer vocabularies and deals with variations in linguistic terms by using a degree of membership

logical positivism

belief that a concept is meaningful only if it can be empirically verified

branching factor effective

b∗ (branching factor)

constraint weighting

can help concentrate the search on the important constraints.

computability

capable of being computed; the ability to solve a problem in an effective manner

thinking rationally

creating provable correct systems. Example: constraint satisfaction problems and expert systems. Problematic in the health field due to a knowledge cliff.

transposition (in a game)

different permutation of the move sequence that end up in the same position

terminal test

in a game search, checks to see if the game is over

IDA* search

iterative-depeening A. Sets the limit to the smallest cost of an successor nodes, and then proceeds in a depth first until we can't find the limit.Saves tremendously on memory, however has a worse time complexity than A*

uncertain environment

lacks so much information that it is difficult to assign probabilities to the likely outcomes of alternatives

complementary literals

one is the negation of the other

problem generator

part of the learning agent, this component suggests future actions that will lead to informative experiences for the learner

game playing

playful or childish exchanges between characters

contingencies

possible outcomes; different plans based on varying circumstances The unpredictability of these other agents can introduce contingencies into the agent's problem-solving process

route finding problem

problem defined in terms of locations and links between them

touring problem

problem defined in terms of locations and links, but also must complete a circuit (so must have the a store of visited cities)

problem optimization

problem for finding what is the optimal values for a particular objective function

constraint optimization problem

problem search in which certain hard constraints must be met, as well as the objective function maximized

task environment

problems spaces for which agents are the solutions. Can be specified through PEAS

factoring

rewriting an expression as the product of its factors

node consistency

single variable (corresponding to a node in the CSP network) is node-consistent if all the values in the variable's domain satisfy the variable's unary constraints.

knowledge-based system

software that uses a specific set of information.

reasoning

the action of thinking about something in a logical, sensible way.

minimax decision

the move that chooses the optimal move for you

node parent

the node connecting others nodes to it.

agent knowledge-based

this approach to intelligence, not by purely reflex mechanisms REASONING but by processes of reasoning that operate on REPRESENTATION internal representations of knowledge

unit resolution

two inference steps are examples of the unit resolution inference rule, pg 252

step cost

cost that increases with volume in steps; also called semifixed cost

monkey and bananas

A 3-foot-tall monkey is in a room where some bananas are suspended from the 8-foot ceiling. He would like to get the bananas. The room contains two stackable, movable, climbable 3-foot-high crates.

proof

A logical argument that shows a statement is true.

generalized arc consistent

A variable Xi is generalized arc consistent with respect to an n-ary constraint if for every value v in the domain of Xi there exists a tuple of values that is a member of the constraint, has all its values taken from the domains of the corresponding variables, and has its Xi component equal to v.

CHESS 4.5

A version of iterative deepening designed to make efficient use of the chess clock was first used by Slate and Atkin (1977) in the CHESS 4.5 game-playing program.

backmarking

Backmarking (Gaschnig, 1979) is a particularly simple method in which consistent and inconsistent pairwise assignments are saved and used to avoid rechecking constraints. Backmarking can be combined with conflict-directed backjumping; Kondrak and van Beek (1997) present a hybrid algorithm that provably subsumes either method taken separately.

goal-directed reasoning

Backward chaining is a form of goal-directed reasoning. It is useful for answering specific questions such as "What shall I do now?" and "Where are my keys?" Often, the cost of backward chaining is much less than linear in the size of the knowledge base, because the process touches only relevant facts.

contour (of a state space)

Because f(n') >= f(n) (i.e., the heuristic function is nondecreasing), we can draw contours around certain node n in the graph and say that all nodes in that contour is less than f(n)

observable

Being able to notice or perceive an event.

positivism logical

Building on the work of Ludwig Wittgenstein (1889-1951) and Bertrand Russell (1872-1970), the famous Vienna Circle, led by Rudolf Carnap (1891-1970), developed the doctrine of logical positivism. This doctrine holds that all knowledge can be characterized by logical theories connected, ultimately, to observation sentences that correspond to sensory inputs; thus logical positivism combines rationalism and empiricism.

LOGISTELLO

Buro applied this technique to his Othello program, LOGISTELLO, and found that a version of his program with PROBCUT beat the regular version 64% of the time, even when the regular version was given twice as much time.

SATZ

By 1995 the SATZ solver (Li and Anbulagan, 1997) could handle 1,000 variables, thanks to optimized data structures for indexing variables.

CNF (Conjunctive Normal Form)

CNF)—that is, a conjunction of clauses, where each clause is a disjunction of literals

channel routing

Channel routing finds a specific route for each wire through the gaps between the cells.

creativity

Chomsky pointed out that the behaviorist theory did not address the notion of creativity in language—it did not explain how a child could understand and make up sentences that he or she had never heard before.

cutset cycle

Choose a subset S of the CSP's variables such that the constraint graph becomes a tree after removal of S. S

search learning to

Could an agent learn how to search better? The answer is yes, and the method rests on an important concept called the metalevel state space.

3-SAT

Crawford and Auton (1993) located the 3-SAT transition at a clause/variable ratio of around 4.26, noting that this coincides with a sharp peak in the run time of their SAT solver.

optimality (of a search algorithm)

Does the strategy find the optimal solution, as defined on page 68?

individual (in genetic algorithms)

Each state, or individual, is represented as a string over a finite alphabet—most commonly, a string of 0s and 1s.

state estimation recursive

Equation (4.6) is called a recursive state estimator because it computes the new belief state from the previous one rather than by examining the entire percept sequence. If the agent is not to "fall behind," the computation has to happen as fast as percepts are coming in. As the environment becomes more complex, the exact update computation becomes infeasible and the agent will have to compute an approximate belief state, perhaps focusing on the implications of the percept for the aspects of the environment that are of current interest. Most work on this problem has been done for stochastic, continuous-state environments with the tools of probability theory

game theory

Evaluates alternate strategies when outcome depends not only on each individual's strategy but also that of others. Von Neumann and Morgenstern's development of game theory (see also Luce and Raiffa, 1957) included the surprising result that, for some games, a rational agent should adopt policies that are (or least appear to be) randomized. Unlike decision theory, game theory does not offer an unambiguous prescription for selecting actions.

complete assignment

Every variable is associated with a value

semantics

Meaning of words and sentences

variable atemporal

Symbols associated with permanent aspects of the world do not need a time superscript and are sometimes called atemporal variables.

xor

exclusive or

MINISAT

See pic

MODEL-BASED-REFLEX-AGENT

See pic

cognitive science

brings together computer models from AI and experimental techniques from psychology to construct precise and testable theories of the human mind. based on experimental investigation of actual humans or animals.

Goal based agent

builds on top of a model-based agent. This agent has not only a working model but a __, that describes a desired final state. With the model, the agent can choose actions that accomplish this __

RBFS

see pic (RECURSIVE-BEST-FIRST-SEARCH)

variable

ways to define the six-dimensional space

search cutting off

if CUTOFF-TEST(state, depth) then return EVAL(state)

node child

node belonging to the parent node

terminal states

set of states in which the game has ended

OR-SEARCH

See Pic

clause

a disjunction of literals

solution optimal

a solution minimizes the path cost

definite clause

A disjunction of literals of which exactly one is positive

singularity

A point in which matter is infinitely dense, as in the center of a black hole or the universe at the very beginning.

factored representation

A possible world is represented by variable/value pairs a way to define the environment as a set of variables / attributes which can have values. Slightly more expressive than atomic representation, but less expressive than a structured representation

problem relaxed

A problem with fewer restrictions on the actions

pure symbol

A pure symbol is a symbol that always appears with the same "sign" in all clauses. For example, in the three clauses (A ∨ ¬B), (¬B ∨ ¬C), and (C ∨ A), the symbol A is pure because only the positive literal appears, B is pure because only the negative literal appears, and C is impure.

conjunctive normal form

A sentence expressed as a conjunction of clauses (disjuncts)

rule implication

A sentence such as (W1,3∧P3,1) ⇒ ¬W2,2 is called an implication (or conditional).

table

An arrangement of data made up of horizontal rows and vertical columns.

truth preserving inference

An inference algorithm that derives only entailed sentences is called sound or truth preserving.

sentence complex

Complex sentences are constructed from simpler sentences, using parentheses and logical connectives.

task network

Exchange of specific job-related resources

cutset conditioning

Finding the smallest cycle cutset is NP-hard, but several efficient approximation algorithms are known. The overall algorithmic approach

incompleteness theorem

Gödel's idea on the inherent limitations of all but the most trivial axiomatic systems capable of doing arithmetic.

environment dynamic

If the environment can change while an agent is deliberating

node

In a search tree, contain the state as well as the associated metadata

data-driven

Interpretations of research that are based on objective results of a project are considered data driven.

completeness of a search algorithm

Is the algorithm guaranteed to find a solution when there is one?

constraint nonlinear

It can be shown that no algorithm exists for solving general nonlinear constraints on integer variables.

nonlinear constraints

It can be shown that no algorithm exists for solving general nonlinear constraints on integer variables.

LRTA*-COST

It can explore an environment of n states in O(n 2) steps in the worst case, but often does much better.

thrashing

Just like OS, SMA* falls pray this problem of swapping memory in and out inefficiently

search beam

Local beam search is an adaptation of beam search, which is a path-based algorithm.

path consistency

Path consistency tightens the binary constraints by using implicit constraints that are inferred by looking at triples of variables

logic

Reasoning conducted or assessed according to strict principles of validity

learning element

Responsible for making improvements.

GENETIC-ALGORITHM

See pic

sentence as physical configuration

Sentences are physical configurations of the agent, and reasoning is a process of constructing new physical configurations from old ones.

knowledge commonsense

Sound and prudent but often unsophisticated judgment.

environment static

Static environments are easy to deal with because the agent need not keep looking at the world while it is deciding on an action, nor need it worry about the passage of time.

instance (of a schema)

Strings that match the schema (such as 24613578) are called instances of the schema.

sentence atomic

The atomic sentences consist of a single proposition symbol.

logicism

The belief that math can be formally reduced to logic.

KB-AGENT

The central component of a knowledge-based agent is its knowledge base

environment continuous

The discrete/continuous distinction applies to the state of the environment, to the way time is handled, and to the percepts and actions of the agent. For example, the chess environment has a finite number of distinct states (excluding the clock). Input from digital cameras is discrete, strictly speaking, but is typically treated as representing continuously varying intensities and locations.

environment discrete

knowledge level

The lowest level of thinking in Bloom's taxonomy in which the learner must only recall information or knowledge. degree to which the selected respondents feel they have knowledge of or experience with the survey's topics

truth maintenance system (TMS)

The most general and powerful form of intelligent backtracking was actually developed very early on by Stallman and Sussman (1977). Their technique of dependency-directed backtracking led to the development of truth maintenance systems (Doyle, 1979), which we discuss in Section 12.6.2. The connection between the two areas is analyzed by de Kleer (1989). Truth maintenance systems, or TMSs, are designed to handle exactly these kinds of complications.

heuristic null move

The most important of these is the null move heuristic, which generates a good lower bound on the value of a position, using a shallow search in which the opponent gets to move twice at the beginning. This lower bound often allows alpha-beta pruning without the expense of a full-depth search.

consistency condition

The original A∗ paper introduced the consistency condition on heuristic functions. The monotone condition was introduced by Pohl (1977) as a simpler replacement, but Pearl (1984) showed that the two were equivalent.

unsatisfiability

The relationship of entailment between sentences is crucial to our understanding of reasoning. A sentence α entails another sentence β if β is true in all worlds where α is true. Equivalent definitions include the validity of the sentence α ⇒ β and the unsatisfiability of the sentence α ∧ ¬β.

variable in continuous state space

The state space is then defined by the coordinates of the airports: (x1, y1), (x2, y2), and (x3, y3). This is a six-dimensional space; we also say that states are defined by six variables. (In general, states are defined by an n-dimensional vector of variables, x.)

halting problem

The theoretical problem of determining whether a computer program will halt (produce an answer) or loop forever on a given input

literal watched

Two crucial contributions were the watched literal indexing technique of Zhang and Stickel (1996), which makes unit propagation very efficient, and the introduction of clause (i.e., constraint) learning techniques from the CSP community by Bayardo and Schrag (1997). Using these ideas, and spurred by the prospect of solving industrial-scale circuit verification problems, Moskewicz et al. (2001) developed the CHAFF solver, which could handle problems with millions of variables.

completeness of a proof procedure

We have described a reasoning process whose conclusions are guaranteed to be true in any world in which the premises are true; in particular, if KB is true in the real world, then any sentence α derived from KB by a sound inference procedure is also true in the real world. So, while an inference process operates on "syntax"—internal physical configurations such as bits in registers or patterns of electrical blips in brains—the process corresponds

problem sensorless

When the agent's percepts provide no information at all, we have what is called a sensorless problem or sometimes a conformant problem. when there is no percept information at all for the agent AKA conformant

search conformant

When the agent's percepts provide no information at all, we have what is called a sensorless problem or sometimes a conformant problem. At first, one might think the sensorless agent has no hope of solving a problem if it has no idea what state it's in; in fact, sensorless problems are quite often solvable. Moreover, sensorless agents can be surprisingly useful, primarily because they don't rely on sensors working properly.

search nondeterministic

When the environment is nondeterministic, percepts tell the agent which of the possible outcomes of its actions has actually occurred. In both cases, the future percepts cannot be determined in advance and the agent's future actions will depend on those future percepts. So the solution to a problem is not a sequence but a contingency plan (also known as a strategy) that specifies what to do depending on what percepts are received.

forward checking

When variable X is assigned, delete any value of constraint-graph neighbor variables inconsistent with the assigned value of X. One of the simplest forms of inference is called forward checking. Whenever a variable X is assigned, the forward-checking process establishes arc consistency for it: for each unassigned variable Y that is connected to X by a constraint, delete from Y 's domain any value that is inconsistent with the value chosen for X. Because forward checking only does arc consistency inferences, there is no reason to do forward checking if we have already done arc consistency as a preprocessing step.

search continuous space

Yet none of the algorithms we have described (except for first-choice hill climbing and simulated annealing) can handle continuous state and action spaces, because they have infinite branching factors.

exclusive or

a binary Boolean function whose output is 1 if its inputs are different. Abbreviated XOR.

propositional logic

a branch of formal, deductive logic in which the basic unit of thought is the proposition. the way in which the truth of sentences is determined.

metaphysics

a branch of philosophy tha tinvestigates the ultimate nature of reality. all meaningful statements can be verified or falsified either by experimentation or by analysis of the meaning of the words. Because this rules out most of metaphysics, as was the intention, logical positivism was unpopular in some circles.

checkers

a checkerboard game for two players who each have 12 pieces

successor function

a description of possible actions available to the agent. The successor function, given a state, returns <action, successor>. a function that returns all possible successors

Horn clause

a disjunction of predicates in which at most one of the predicates is not negated which is a disjunction of literals of which at most one is positive. So all definite clauses are Horn clauses, as are clauses with no positive literals; these are called goal clauses. Horn clauses are closed under resolution: if you resolve two Horn clauses, you get back a Horn clause.

resolution

a firm decision to do or not to do something.

plateau (in local search)

a flat area of a state-space landscape. A shoulder is a plateau that is not a local maximum

actuator

a mechanism that puts something into automatic action

priority queue

a queue in which the highest-priority elements are removed first; within a priority value, the earliest arrival is removed first. a queue that pops the element with the highest priority

model-based reflex agents

a rational agent that uses precept history to create a model of the current state of the world

utility expected

a rational utility-based agent chooses the action that maximizes the expected utility of the action outcomes—that is, the utility the agent expects to derive, on average, given the probabilities and utilities of each outcome.

learning

a relatively permanent change in behavior due to experience

parallel search

a search in which multiple stimuli are processed at the same time.

cyclic solution

a solution for a nondeterministic problem that requires a cycle label: how to represent a cycle in a cyclic solution

consistency of a CSP assignment

a solution to a CSP is a consistent, complete assignment.

Hubble Space Telescope

a space telescope and camera named for a famous astronaut (Edward Hubble) used to study space elements. The scheduling of experiments on the Hubble Space Telescope requires very precise timing of observations; the start and finish of each observation and maneuver are continuous-valued variables that must obey a variety of astronomical, precedence, and power constraints. The best-known category of continuous-domain CSPs is that of linear programming problems, where constraints must be linear equalities or inequalities. Linear programming problems can be solved in time polynomial in the number of variables. Problems with different types of constraints and objective functions have also been studied—quadratic programming, second-order conic programming, and so on.

queue FIFO

a standard queue, popping the oldest out

Perceptron representational power

a two-input perceptron (restricted to be simpler than the form Rosenblatt originally studied) could not be trained to recognize when its two inputs were different.

axiom

a universally recognized principle; premise; postulate; self-evident truth

minimum-remaining-values

after SA is assigned, the choices for Q, NSW, and V are all forced. This intuitive idea—choosing the variable with the fewest "legal" values—is called the minimum-remaining-values (MRV) heuristic. It also has been called the "most constrained variable" or "fail-first" heuristic, the latter because it picks a variable that is most likely to cause a failure soon, thereby pruning the search tree. If some variable X has no legal values left, the MRV heuristic will select X and failure will be detected immediately—avoiding pointless searches through other variables. The MRV heuristic usually performs better than a random or static ordering, sometimes by a factor of 1,000 or more, although the results vary widely depending on the problem.

execution

after a solution is found, the agent can began the action sequence. It's call this phase

solution

after the process of looking at sequences of possible actions (or a search), the search algorithm finds this in the terms a fixed action sequence works best in a static environment

queue LIFO

also known as a stack, popping out the newest

environment multiagent

an agent playing chess is in a twoagent environment.

environment single-agent

an agent solving a crossword puzzle by itself

omniscience agent

an agent that is all knowing

no-good

an informal way of saying not suitable or not acceptable. Constraint learning is the idea of finding a minimum set of variables from the conflict set that causes the problem. This set of variables, along with their corresponding values, is called a no-good. We then record the no-good, either by adding a new constraint to the CSP or by keeping a separate cache of no-goods.

iterative lengthening search

an iterative analog of uniform cost search. The idea is to use increasing limits on path cost. If a node is generated whose path cost exceeds the current limit, it is immediately discarded.

taxonomic hierarchy

an ordered series of progressively smaller categories Domain, Kingdom, Phylum, Class, Order, Family, Genus, Species

expected utility

average utility based on a probability distribution

consistency of a heuristic

being influenced by our past active, voluntary, and public commitments during peripheral processing of persuasive messages

Qubic

checkers joins Qubic (Patashnik, 1980), Connect Four (Allis, 1988), and Nine-Men's Morris (Gasser, 1998) as games that have been solved by computer analysis.

mutation

each location is subject to random mutation with a small independent probability.

episodic environment

each sequence of action is independent from the others ex: spotting defective parts in an assembly line

state repeated

nodes that already have been expanded that are part of the frontier again. Generating them is called generating a loopy path

8-queens problem

eight queens on a chessboard so they don't attack

uncertainty

either not fully observable OR not deterministic

static environment

environmental factors are stable over time. if the environment cannot change while the agent is deliberating. Ex: Our particular Roomba simulation

performance measure

evaluates any given sequence of environment states.

sliding-block puzzle

example is the 8-puzzle. Randomized positions for numbers, try the moves that bring them back in order

neuroscience

how the body and brain enable emotions, memories, and sensory experiences

derived sentences

if KB is true in the real world, then any sentence α derived from KB by a sound inference procedure is also true in the real world

Maximum global

if elevation corresponds to an objective function, then the aim is to find the highest peak—a global maximum.

constraint preference constraint

indicating which solutions are preferred.

pruning in contingency problems

like alpha-beta pruning could be applied to game trees with chance nodes

rationality

logic and reasoning

mathematics

math; algorithms

pruning forward

meaning that some moves at a given node are pruned immediately without further consideration

internal state

memory is better when your internal state is the same as at the time of learning. any memory that is within an agent, for storing precept history.

multiagent environments

more than one agent in the environment ex: chess, or taxi driving

retrograde

moving backward; having a backward motion or direction; retiring or retreating. retrograde minimax search: reverse the rules of chess to do unmoves rather than moves.

square roots

multiplying a number by itself

negative literal

negated atomic sentence

irreversible

not able to change back to a former state; impossible to revert back. leads to a infinite competitive ratio. If some actions that an exploring agent takes cannot be untaken.

minimum local

not maximum local

optimally efficient algorithm

of all optimal algorithms with a particular heuristic function, A* is the most efficient one

local search

operate using a single current node and move to neighbors of that node

search local

operate using a single current node and move to neighbors of that node

random walk

randomly chooses action, will eventually find the goal or complete exploration given a finite space

environment game-playing

real-time, multiplayer game-playing environment, where time is part of the environment state and players are given fixed time allocations.

metareasoning

reasoning about what computation to do. An example of this is alpha-beta pruning, in which we decided to do only a subset of computations because it was more efficient. "reasoning about reasoning"

spatial

relating to space

game tree

represents state of the game and all possible next states after all actions a player or opponent takes. Can be very large

precedence constraints

restrictions on order in which work elements can be performed

search bidirectional

search forward on the initial node, and backwards on the goal node

search uninformed

search strategies that have no additional information about the states provided beyond the problem definitionAKA blind search

minimax search

see minimax algorithm

thinking humanly

simulating and emulating the thought processes of humans. Example: neural networks

software agent

small piece of software that acts on your behalf. agents that exist only in the software world. Like the Amazon recommendation engine

complete-state formulation

start with the full complete state but then find a goal stateEx: 8-queens problem, start with all 8 queens on the board, and move them such that none of the queens are attacked

state space

state of states reachable from the initial state by a sequence of actions. Defined by: initial state, actions, and transition model It forms a graph. A path in the graph is the sequence of states connected by a sequence of actions

state world

state, the agent's current conception of the world state

world state

state, the agent's current conception of the world state

causation

the belief that events occur in predictable ways and that one event leads to another

logical reasoning

the idea that there are principles governing correct or reliable inferences

commitment epistemological

the possible states of knowledge that it allows with respect to each fact.

frame problem inferential

the problem of projecting forward the results of a t step plan of action in time O(kt) rather than O(nt).

truth value

the truth or falsity of a statement

constraint unary

the value of a single variable.

reward

to give something it has earned. the performance standard distinguishes part of the incoming percept as a reward (or penalty) that provides direct feedback on the quality of the agent's behavior.

conflict clause learning

to record conflicts so that they won't be repeated later in the search. Usually a limited-size set of conflicts is kept, and rarely used ones are dropped.

thought

to think

NLP (natural language processing)

uses expert or artificial intelligence software to automatically assign code numbers

RECURSIVE-BEST-FIRST-SEARCH

uses f-limit variable to keep track of best alternative path from any ancestor of the current node. Replaces f-value with best f-value of nodes children. See pic

formulate search execute

we have a simple "formulate, search, execute" design for the agent, as shown in Figure 3.1. After formulating a goal and a problem to solve, the agent calls a search procedure to solve it. It then uses the solution to guide its actions, doing whatever the solution recommends as the next thing to do—typically, the first action of the sequence—and then removing that step from the sequence. Once the solution has been executed, the agent will formulate a new goal.

generation (of states)

we need to consider taking various actions. We do this by expanding the current state; that is, applying each legal action to the current state, thereby generating a new set of states.

semantics logical

which defines the truth of each sentence in each possible world or model.

utility

"the quality of being useful,"; The mathematical treatment of "preferred outcomes" or utility was first formalized by L´eon Walras (pronounced "Valrasse") (1834-1910) and was improved by Frank Ramsey (1931) and later by John von Neumann and Oskar Morgenstern in their book The Theory of Games and Economic Behavior (1944).

planning contingency

(also known as a strategy) that specifies what to do depending on what percepts are received.

payoff function

-What determines a player's payoff given the combination of actions by the other players A utility function (also called an objective function or payoff function)

optimality

1. Allocative efficiency of resources. 2. Efficient distribution of output. 3. Producing what is demanded. One of the 4 measures of problem-solving performance: is the solution that is found optimal?

problem VLSI layout

A VLSI layout problem requires positioning millions of components and connections on a chip to minimize area, minimize circuit delays, minimize stray capacitances, and maximize manufacturing yield. The layout problem comes after the logical design phase and is usually split into two parts: cell layout and channel routing. In cell layout, the primitive components of the circuit are grouped into cells, each of which performs some recognized function. Each cell has a fixed footprint (size and shape) and requires a certain number of connections to each of the other cells. The aim is to place the cells on the chip so that they do not overlap and so that there is room for the connecting wires to be placed between the cells. Channel routing finds a specific route for each wire through the gaps between the cells. These search problems are extremely complex, but definitely worth solving. Later in this chapter, we present some algorithms capable of solving them.

conflict-directed backjumping

A backjumping algorithm that uses conflict sets defined in this way

prior knowledge

A combination of attitudes, experiences, and information that you already know about a particular topic that help you make connections as you read.

Davis-Putnam algorithm

A complete backtracking algorithm. The first algorithm we consider is often called the Davis-Putnam algorithm, after the seminal paper by Martin Davis and Hilary Putnam (1960). The algorithm is in fact the version described by Davis, Logemann, and Loveland (1962), so we will call it DPLL after the initials of all four authors. DPLL takes as input a sentence in conjunctive normal form—a set of clauses. Like BACKTRACKING-SEARCH and TT-ENTAILS?, it is essentially a recursive, depth-first enumeration of possible models. It embodies three improvements over the simple scheme of TT-ENTAILS?: 1. Early termination 2. Pure symbol heuristic 3. Unit clause heuristic

transition model

A component of a problem: a function that maps a state and action to a next state

goal test

A component of a problem: determines whether the current state is a goal

g (path cost)

A component of a problem: the cost, traditionally denoted by g(n), of the path from the initial state to the node, as indicated by the parent pointers. a function that assigned a numeric cost for a path. The step cost is the cost of taking an action from one particular state to the next

GPS (General Problem Solver)

A computer program developed by Allan Newell and Herbert Simon that solved problems in cryptarithmetic and logic using means-ends analysis

tree

A connected graph with no cycles

open-loop

A control system that has no means for comparing the output with input for control purposes. defines a system that ignores percepts while executing an action

refutation

A denial of the validity of an opposing argument. In order to sound reasonable, refutations often follow a concession that acknowledges that an opposing argument may be true or reasonable. proof by contradiction.

value

A factored representation splits up each state into a fixed set of variables or attributes, each of which can have a value.

Representation factored

A factored representation splits up each state into a fixed set of variables or attributes, each of which can have a value. While two different atomic states have nothing in common—they are just different black boxes—two different factored states can share some attributes (such as being at some particular GPS location) and not others (such as having lots of gas or having no gas); this makes it much easier to work out how to turn one state into another. With factored representations, we can also represent uncertainty—for example, ignorance about the amount of gas in the tank can be represented by leaving that attribute blank. Many important areas of AI are based on factored representations, including constraint satisfaction algorithms (Chapter 6), propositional logic (Chapter 7), planning (Chapters 10 and 11), Bayesian networks (Chapters 13-16), and the machine learning algorithms in Chapters 18, 20, and 21.

path cost

A function that assigns a numeric cost to each path. Step cost is the cost of moving from a state by an action to its successor. A component of a problem: a function that assigned a numeric cost for a path. The step cost is the cost of taking an action from one particular state to the next

game of imperfect information

A game in which some player must make a move but is unable to observe the earlier or simultaneous move of some other player games such as backgammon that include an element of chance; we also discuss bridge, which includes elements of imperfect information because not all cards are visible to each player.

game of chance

A game involving luck versus skill; any contect, drawing

monotonicity of a heuristic

A heuristic h(n) is consistent if, for every node n and every successor n of n generated by any action a, the estimated cost of reaching the goal from n is no greater than the step cost of getting to n plus the estimated cost of reaching the goal from n : h(n) ≤ c(n, a, n ) + h(n) .

constraint hypergraph

A hypergraph consists of ordinary nodes (the circles in the figure) and hypernodes (the squares), which represent n-ary constraints.

sentence in a KB

A knowledge base is a set of sentences. (Here "sentence" is used as a technical term. It is related but not identical to the sentences of English and other natural languages.)

declarativism

A knowledge-based agent can be built simply by TELLing it what it needs to know. Starting with an empty knowledge base, the agent designer can TELL sentences one by one until the agent knows how to operate in its environment.

literal (sentence)

A literal is either an atomic sentence (a positive literal) or a negated atomic sentence (a negative literal).

CHILD-NODE

A node in a binary tree below another node

OR node

A node in an AND-OR tree that represents the current state in the problem. At this node, we could choose next possible actions.

partial assignment

A partial assignment is one that assigns values to only some of the variables.

path

A path in the state space is a sequence of states connected by a sequence of actions.

model

A pattern, plan, representation, or description designed to show the structure or workings of an object, system, or concept

problem real-world

A real-world problem is one whose solutions people actually care about. Such problems tend not to have a single agreed-upon description, but we can give the general flavor of their formulations. compared to a toy-problem, is one whose solutions people actually care about

syntax of logic

A representation language is defined by its syntax, which specifies the structure of sentences, and its semantics, which defines the truth of each sentence in each possible world or model.

vacuum tube

A sealed glass tube from which most of the air has been evacuated

satisfiability

A sentence is satisfiable if it is true in, or satisfied by, some model. For example, the knowledge base given earlier, (R1 ∧ R2 ∧ R3 ∧ R4 ∧ R5), is satisfiable because there are three models in which it is true, as shown in Figure 7.9. Satisfiability can be checked by enumerating the possible models until one is found that satisfies the sentence.

disjunct

A sentence using ∨, such as (W1,3∧P3,1)∨W2,2, is a disjunction of the disjuncts (W1,3 ∧ P3,1) and W2,2. (Historically, the ∨ comes from the Latin "vel," which means "or." For most people, it is easier to remember ∨ as an upside-down ∧.)

disjunction

conjunct

A sentence whose main connective is ∧, such as W(1,3) ∧ P(3,1), is called a conjunction; its parts are the conjuncts.

conjunction (logic)

A sentence whose main connective is ∧, such as W(1,3) ∧ P(3,1), is called a conjunction; its parts are the conjuncts.

card games

A series of uniquely printed cards used within set rules of a game.

Turing Test

A test proposed by Alan Turing in which a machine would be judged "intelligent" if the software could use a chat conversation to fool a human into thinking it was talking with a person instead of a machine.

problem toy

A toy problem is intended to illustrate or exercise various problem-solving methods. It can be given a concise, exact description and hence is usable by different researchers to compare the performance of algorithms.

greedy search

A type of best-first search. search based on a priority queue, where the evaluation function f(n) = h(n) (i.e., the heuristic)

possible world

A way the real world could have been; a complete state of affairs.

SMA∗

A* but we have a set memory space. We expand best nodes until we run out of memory; then we delete the old nodes.

theorem proving mathematical

AI currently encompasses a huge variety of subfields, ranging from the general (learning and perception) to the specific, such as playing chess, proving mathematical theorems, writing poetry, driving a car on a crowded street, and diagnosing diseases. AI is relevant to any intellectual task; it is truly a universal field.

limited rationality

Acting appropriately when there is not enough time to do all the computations one might like.

tractability of inference

Although decidability and computability are important to an understanding of computation, the notion of tractability has had an even greater impact. Roughly speaking, a problem is called intractable if the time required to solve instances of the problem grows exponentially with the size of the instances. The distinction between polynomial and exponential growth in complexity was first emphasized in the mid-1960s (Cobham, 1964; Edmonds, 1965). It is important because exponential growth means that even moderately large instances cannot be solved in any reasonable time. Therefore, one should strive to divide the overall problem of generating intelligent behavior into tractable subproblems rather than intractable ones.

million queens problem

Amazingly, on the n-queens problem, if you don't count the initial placement of queens, the run time of min-conflicts is roughly independent of problem size. It solves even the million-queens problem in an average of 50 steps (after the initial assignment). This remarkable observation was the stimulus leading to a great deal of research in the 1990s on local search and the distinction between easy and hard problems, which we take up in Chapter 7. Roughly speaking, n-queens is easy for local search because solutions are densely distributed throughout the state space. Min-conflicts also works well for hard problems.

LRTA*-AGENT

An LRTA∗ agent is guaranteed to find a goal in any finite, safely explorable environment.

NP-complete

An NP problem X for which it is possible to reduce any other NP problem Y to X in polynomial time. Intuitively this means that we can solve Y quickly if we know how to solve X quickly. Precisely, Y is reducible to X if there is a polynomial time algorithm f to transform instances y of Y to instances x = f(y) of X in polynomial time with the property that the answer to y is yes if and only if the answer to f(y) is yes.

problem conformant

At first, one might think the sensorless agent has no hope of solving a problem if it has no idea what state it's in; in fact, sensorless problems are quite often solvable. Moreover, sensorless agents can be surprisingly useful, primarily because they don't rely on sensors working properly.

Remote Agent

Autonomous planning and scheduling: A hundred million miles from Earth, NASA's Remote Agent program became the first on-board autonomous planning program to control the scheduling of operations for a spacecraft (Jonsson et al., 2000). REMOTE AGENT generated plans from high-level goals specified from the ground and monitored the execution of those plans—detecting, diagnosing, and recovering from problems as they occurred.

vacuum world slippery

Consider the slippery vacuum world, which is identical to the ordinary (non-erratic) vacuum world except that movement actions sometimes fail, leaving the agent in the same location. For example, moving Right in state 1 leads to the state set {1, 2}. Figure 4.12 shows part of the search graph; clearly, there are no longer any acyclic solutions from state 1, and AND-OR-GRAPH-SEARCH would return with failure. There is, however, a cyclic solution, which is to keep trying Right until it works. We can express this solution by adding a label to denote some portion of the plan and using that label later instead of repeating the plan itself. Thus, our cyclic solution is [Suck, L1 : Right , if State =5 then L1 else Suck] . (A better syntax for the looping part of this plan would be "while State =5 do Right .")

global constraint

Constraint involving an arbitrary number of variables

iterative deepening search

DLS, with levels going from 1 to infinity until we found the goal node Is complete Is optimal (on same conditions as BFS) TC: O(b^d) SC: O(bd)

search iterative deepening

DLS, with levels going from 1 to infinity until we found the goal node Is complete Is optimal (on same conditions as BFS) TC: O(b^d) SC: O(bd)

Graph Eulerian

Depth-first search fails with irreversible actions; the more general problem of exploring Eulerian graphs (i.e., graphs in which each node has equal numbers of incoming and outgoing edges) was solved by an algorithm due to Hierholzer (1873). The first thorough algorithmic study of the exploration problem for arbitrary graphs was carried out by Deng and Papadimitriou (1990), who developed a completely general algorithm but showed that no bounded competitive ratio is possible for exploring a general graph. Papadimitriou and Yannakakis (1991) examined the question of finding paths to a goal in geometric path-planning environments (where all actions are reversible). They showed that a small competitive ratio is achievable with square obstacles, but with general rectangular obstacles no bounded ratio can be achieved.

heavy-tailed distribution

Distribution that differs from a normal curve by being too spread out so that a histogram of the distribution would have too many scores at each of the two extremes ("tails")

domination (of heuristics)

Domination translates directly into efficiency: A∗ using h2 will never expand more nodes than A∗ using h1 (except possibly for some nodes with f(n)=C∗). it is generally better to use a heuristic function with higher values, provided it is consistent and that the computation time for the heuristic is not too long.

dynamic programming

Dynamic programming involves using past history to develop a model that depicts the time taken to complete a certain project task. When new values are input, the model generates output that tells you how long a new project is likely to take.

knowledge representation language

Each sentence is expressed in a language called a knowledge representation language and represents some assertion about the world. Sometimes we dignify a sentence with the name axiom, when the sentence is taken as given without being derived from other sentences.

learning metalevel

diameter (of a graph)

For any graph, this give the maximum node hops to go from one arbitrary node to another. for depth-limited search, this gives us an ideal choice for l. We would discover that any city can be reached from any other city in at most 9 steps. This number, known as the diameter of the state space, gives us a better depth limit, which leads to a more efficient depth-limited search. For most problems, however, we will not know a good depth limit until we have solved the problem.

rational agent

For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has. an agent that does the right thing for any particular percept sequence, by maximizing a particular performance measure, it's dependent on what given knowledge the agent has

problem underconstrained

For example, the n-queens problem—thought to be quite tricky for backtracking search algorithms—turned out to be trivially easy for local search methods, such as min-conflicts. This is because solutions are very densely distributed in the space of assignments, and any initial assignment is guaranteed to have a solution nearby. Thus, n-queens is easy because it is underconstrained.

synapse

Gap between neurons

Go (game)

Go is the most popular board game in Asia. Because the board is 19 × 19 and moves are allowed into (almost) every empty square, the branching factor starts at 361, which is too daunting for regular alpha-beta search methods. In addition, it is difficult to write an evaluation function because control of territory is often very unpredictable until the endgame. Therefore the top programs, such as MOGO, avoid alpha-beta search and instead use Monte Carlo rollouts. The trick is to decide what moves to make in the course of the rollout. There is no aggressive pruning; all moves are possible. The UCT (upper confidence bounds on trees) method works by making random moves in the first few iterations, and over time guiding the sampling process to prefer moves that have led to wins in previous samples. Some tricks are added, including knowledge-based rules that suggest particular moves whenever a given pattern is detected and limited local search to decide tactical questions.

game Go

search best-first

Greedy best-first search8 tries to expand the node that is closest to the goal, on the grounds that this is likely to lead to a solution quickly. Thus, it evaluates nodes by using just the heuristic function; that is, f(n) = h(n).

search greedy best-first

Socrates

Greek philosopher; socratic method--questioning; sentenced to death for corrupting Athens youth

theorem incompleteness

Gödel's idea on the inherent limitations of all but the most trivial axiomatic systems capable of doing arithmetic.

logic propositional semantics

Having specified the syntax of propositional logic, we now specify its semantics. The semantics defines the rules for determining the truth of a sentence with respect to a particular model. In propositional logic, a model simply fixes the truth value—true or false—for every proposition symbol.

heuristic search

Heuristic search is an AI search technique that employs heuristic for its moves. Heuristic is a rule of thumb that probably leads to a solution. Strategies that know whether one non-goal state is "more promising" than another are called informed search or heuristic search strategies

search local greedy

Hill climbing is sometimes called greedy local search because it grabs a good neighbor state without thinking ahead about where to go next. Although greed is considered one of the seven deadly sins, it turns out that greedy algorithms often perform quite well

complexity time

How long does it take to find a solution? Time is often measured in terms of the number of nodes generated during the search, and space in terms of the maximum number of nodes stored in memory.

complexity space

How much memory is needed to perform the search? In AI, the graph is often represented implicitly by the initial state, actions, and transition model and is frequently infinite. For these reasons, complexity is expressed in terms of three quantities: b, the branching factor or maximum number of successors of any node; d, the depth of the shallowest goal DEPTH node (i.e., the number of steps along the path from the root); and m, the maximum length of any path in the state space.

game programs

In 1965, the Russian mathematician Alexander Kronrod called chess "the Drosophila of artificial intelligence." John McCarthy disagrees: whereas geneticists use fruit flies to make discoveries that apply to biology more broadly, AI has used chess to do the equivalent of breeding very fast fruit flies. Perhaps a better analogy is that chess is to AI as Grand Prix motor racing is to the car industry: state-of-the-art game programs are blindingly fast, highly optimized machines that incorporate the latest engineering advances, but they aren't much use for doing the shopping or driving off-road. Nonetheless, racing and game-playing generate excitement and a steady stream of innovations that have been adopted by the wider community.

frame problem representational

In a world with m different actions and n fluents, the set of frame axioms will be of size O(mn).

problem robot navigation

In addition to the complexity of the problem, real robots must also deal with errors in their sensor readings and motor controls.

Representation atomic

In an atomic representation each state of the world is indivisible—it has no internal structure. Consider the problem of finding a driving route from one end of a country to the other via some sequence of cities.

connected component

In an undirected graph, a connected component is a maximal set of vertices such that there is a path between every pair of vertices (the example shows 3 connected components). Independence can be ascertained simply by finding connected components of the constraint graph. Each component corresponds to a subproblem CSP.

heuristic min-conflicts

In choosing a new value for a variable, the most obvious heuristic is to select the value that results in the minimum number of conflicts with other variables—the min-conflicts heuristic.

state estimation

In partially observable environments—which include the vast majority of real-world environments—maintaining one's belief state is a core function of any intelligent system. This function goes under various names, including monitoring, filtering and state estimation.

initial state

In problem solving, the conditions at the beginning of a problem. A component of a problem: the state that the agent starts in

stochastic games

In real life, many unpredictable external events can put us into unforeseen situations. Many games mirror this unpredictability by including a random element, such as the throwing of dice. We call these stochastic games.

evolution strategies

In the 1950s, several statisticians, including Box (1957) and Friedman (1959), used evolutionary techniques for optimization problems, but it wasn't until Rechenberg (1965) introduced evolution strategies to solve optimization problems for airfoils that the approach gained popularity.

vacuum world erratic

In the erratic vacuum world, the Suck action works as follows: • When applied to a dirty square the action cleans the square and sometimes cleans up dirt in an adjacent square, too. • When applied to a clean square the action sometimes deposits dirt on the carpet.

search tabu

In the field of operations research, a variant of hill climbing called tabu search has gained popularity (Glover and Laguna, 1997). This algorithm maintains a tabu list of k previously visited states that cannot be revisited; as well as improving efficiency when searching graphs, this list can allow the algorithm to escape from some local minima.

tabu search

environment cooperative

In the taxi-driving environment, on the other hand, avoiding collisions maximizes the performance measure of all agents, so it is a partially cooperative multiagent environment.

feature (of a state)

Inductive learning methods work best when supplied with features of a state that are relevant to predicting the state's value, rather than with just the raw state description.

informed search

Informed methods are given additional information about the goal; Greedy, A* search. strategies that know when one goal state might be more promising than the other AKA heuristic search Algorithm: uses node info to look for solutions Ie, uses node info or heuristics to decide what to expand next

stochastic beam search

Instead of selecting the best k individuals, choose randomly, probability of being selected is proportional to its fitness. like stochastic search, this is a variation of local beam search in which the next k chosen nodes is chosen randomly based on their fitness

Backtracking intelligent

Intelligent backtracking: Looking backward A more intelligent approach to backtracking is to backtrack to a variable that might fix the problem—a variable that was responsible for making one of the possible values of SA impossible.

intelligent backtracking

IBM

International Business Machines, was part of the historic shift to a mass consumer economy after World War II, and symbolized another momentous transformation to the fast-paced "Information Age." the field would be dominated by these people and their students and colleagues at MIT, CMU, Stanford, and IBM.

gold

It is a reward. The only mitigating feature of this bleak environment is the possibility of finding a heap of gold.

optimization convex

It is a special case of the more general problem of convex optimization, which allows the constraint region to be any convex region and the objective to be any function that is convex within the constraint region. Under certain conditions, convex optimization problems are also polynomially solvable and may be feasible in practice with thousands of variables. Several important problems in machine learning and control theory can be formulated as convex optimization problems (see Chapter 20).

CHINOOK

Jonathan Schaeffer CHECKERS and colleagues developed CHINOOK, which runs on regular PCs and uses alpha-beta search. Chinook defeated the long-running human champion in an abbreviated match in 1990, and since 2007 CHINOOK has been able to play perfectly by using alpha-beta search combined with a database of 39 trillion endgame positions.

interior-point method

Karmarkar (1984) developed the far more efficient family of interior-point methods, which was shown to have polynomial complexity for the more general class of convex optimization problems by Nesterov and Nemirovski (1994).

solving games

Knowing how to get to the goal. If the program knew in advance all the dice rolls that would occur for the rest of the game, solving a game with dice would be just like solving a game without dice, which minimax does in O(bm) time, where b is the branching factor and m is the maximum depth of the game tree. Because expectiminimax is also considering all the possible dice-roll sequences, it will take O(bmnm), where n is the number of distinct rolls.

Knowledge base. A knowledge base is a set of sentences.

knowledge base (KB)

Knowledge base. A knowledge base is a set of sentences.

composite decision process

Kumar and Kanal (1988) attempt a "grand unification" of heuristic search, dynamic programming, and branch-and-bound techniques under the name of CDP—the "composite decision process."

population (in genetic algorithms)

Like beam searches, GAs begin with a set of k randomly generated states, called the population. Each state, or individual, is represented as a string over a finite alphabet—most commonly, a string of 0s and 1s.

search local for CSPs

Local search algorithms (see Section 4.1) turn out to be effective in solving many CSPs. They use a complete-state formulation: the initial state assigns a value to every variable, and the search changes the value of one variable at a time.

node current in local search

Local search algorithms operate using a single current node (rather than multiple paths) and generally move only to neighbors of that node. Typically, the paths followed by the search are not retained

notation logical

logic notation

Logicians in the 19th century developed a precise notation for statements about all kinds of objects in the world and the relations among them. (Contrast this with ordinary arithmetic notation, which provides only for statements about numbers.) By 1965, programs existed that could, in principle, solve any solvable problem described in logical notation. (Although if no solution exists, the program might loop forever.) The so-called logicist tradition within artificial intelligence hopes to build on such programs to create intelligent systems.

path redundant

Loopy paths are a special case of the more general concept of redundant paths, which exist whenever there is more than one way to get from one state to another. another path that leads to the same node, that is usually longer than the previous path

problem million queens

UCT (upper confidence bounds on trees)

Monte Carlo methods based on the UCT (upper confidence bounds on trees) scheme (Kocsis and Szepesvari, 2006). The UCT (upper confidence bounds on trees) method works by making random moves in the first few iterations, and over time guiding the sampling process to prefer moves that have led to wins in previous samples. Some tricks are added, including knowledge-based rules that suggest particular moves whenever a given pattern is detected and limited local search to decide tactical questions.

Scrabble

Most people think the hard part about Scrabble SCRABBLE is coming up with good words, but given the official dictionary, it turns out to be rather easy to program a move generator to find the highest-scoring move (Gordon, 1994). That doesn't mean the game is solved, however: merely taking the top-scoring move each turn results in a good but not expert player. The problem is that Scrabble is both partially observable and stochastic: you don't know what letters the other player has or what letters you will draw next. So playing Scrabble well combines the difficulties of backgammon and bridge. Nevertheless, in 2006, the QUACKLE program defeated the former world champion, David Boys, 3-2.

game Scrabble

Most people think the hard part about Scrabble is coming up with good words, but given the official dictionary, it turns out to be rather easy to program a move generator to find the highest-scoring move (Gordon, 1994). That doesn't mean the game is solved, however: merely taking the top-scoring move each turn results in a good but not expert player. The problem is that Scrabble is both partially observable and stochastic: you don't know what letters the other player has or what letters you will draw next. So playing Scrabble well combines the difficulties of backgammon and bridge. Nevertheless, in 2006, the QUACKLE program defeated the former world champion, David Boys, 3-2.

unit propagation

Notice also that assigning one unit clause can create another unit clause—for example, when C is set to false, (C ∨ A) becomes a unit clause, causing true to be assigned to A. This "cascade" of forced assignments is called unit propagation.

multiagent systems

Noting that a collection of agent programs designed to work well together in a true multiagent environment necessarily exhibits modularity—the programs share no internal state and communicate with each other only through the environment—it is common within the field of multiagent systems to design the agent program of a single agent as a collection of autonomous sub-agents. In some cases, one can even prove that the resulting system gives the same optimal solutions as a monolithic design.

chance of winning

One might well wonder about the phrase "chances of winning." After all, chess is not a game of chance: we know the current state with certainty, and no dice are involved. But if the search must be cut off at nonterminal states, then the algorithm will necessarily be uncertain about the final outcomes of those states. This type of uncertainty is induced by computational, rather than informational, limitations. Given the limited amount of computation that the evaluation function is allowed to do for a given state, the best it can do is make a guess about the final outcome.

completeness

One of the 4 measures of problem-solving performance: If the solution can be found, it will be found

time complexity

One of the 4 measures of problem-solving performance: how long does it take to find the solution?

singular extension

One strategy to mitigate the horizon effect is the singular extension, a move that is "clearly better" than all other moves in a given position. Once discovered anywhere in the tree in the course of a search, this singular move is remembered. When the search reaches the normal depth limit, the algorithm checks to see if the singular extension is a legal move; if it is, the algorithm allows the move to be considered. This makes the tree deeper, but because there will be few singular extensions, it does not add many total nodes to the tree.

problem halting

Our proof procedure can go on and on, generating more and more deeply nested terms, but we will not know whether it is stuck in a hopeless loop or whether the proof is just about to pop out.

expansion (of states)

Our successor function. Takes a node and generates the next states The originating node is called the parent node and the generated nodes are called the child nodes

game partially observable

Partially observable games share these characteristics and are thus qualitatively different from the games described in the preceding sections. Chess has often been described as war in miniature, but it lacks at least one major characteristic of real wars, namely, partial observability. In the "fog of war," the existence and disposition of enemy units is often unknown until revealed by direct contact. As a result, warfare includes the use of scouts and spies to gather information and the use of concealment and bluff to confuse the enemy.

Grand Prix

Perhaps a better analogy is that chess is to AI as Grand Prix motor racing is to the car industry: state-of-the-art game programs are blindingly fast, highly optimized machines that incorporate the latest engineering advances, but they aren't much use for doing the shopping or driving off-road.

frame problem

Problem of representing the real world in a computationally tractable way

Difference Engine

Produced navigational tables for ships; Charles Babbage

interleaving

Purposely putting space between the same activity. An agent that acts before it has found guaranteed plan in an nondeterministic environment is doing this

iterative expansion

RBFS (Korf, 1993) is actually somewhat more complicated than the algorithm shown in Figure 3.26, which is closer to an independently developed algorithm called iterative expansion (Russell, 1992). RBFS uses ITERATIVE a lower bound as well as the upper bound; the two algorithms behave identically with admissible heuristics, but RBFS expands nodes in best-first order even with an inadmissible heuristic.

hill climbing random-restart

Random-restart hill climbing adopts the well-known adage, "If at first you don't succeed, try, try again." It conducts a series of hill-climbing searches from randomly generated initial states, until a goal is found. It is trivially complete with probability approaching 1, because it will eventually generate a goal state as the initial state.

search recursive best-first (RBFS)

Recursive best-first search (RBFS) is a simple recursive algorithm that attempts to mimic the operation of standard best-first search, but using only linear space. The algorithm is shown in Figure 3.26. Its structure is similar to that of a recursive depth-first search, but rather than continuing indefinitely down the current path, it uses the f limit variable to keep track of the f-value of the best alternative path available from any ancestor of the current node. If the current node exceeds this limit, the recursion unwinds back to the alternative path. As the recursion unwinds, RBFS replaces the f-value of each node along the path with a backed-up value—the best f-value of its children. In this way, RBFS remembers the f-value of the best leaf in the forgotten subtree and can therefore decide whether it's worth reexpanding the subtree at some later time.

grid rectangular

Route finding on a rectangular grid (like the one used later for Figure 3.9) is a particularly important example in computer games. In such a grid, each state has four successors, so a search tree of depth d that includes repeated states has 4d leaves; but there are only about 2d 2 distinct states within d steps of any given state. For d = 20, this means about a trillion nodes but only about 800 distinct states. Thus, following redundant paths can cause a tractable problem to become intractable. This is true even for algorithms that know how to avoid infinite loops.

inference rule

Rule used by the inference engine in an expert system to describe the relationship between key concepts.

HYBRID-WUMPUS-AGENT

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AND-OR graph

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Geometry Theorem Prover

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ITERATIVE-DEEPENING-SEARCH

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MAX-VALUE

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MIN-CONFLICTS

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MIN-VALUE

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MINIMAX-DECISION

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ONLINE-DFS-AGENT

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PL-FC-ENTAILS?

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PL-RESOLUTION

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PLAN-ROUTE

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PLANNER

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RECURSIVE-DLS

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REFLEX-VACUUM-AGENT

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REPRODUCE

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Romania

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SIMULATED-ANNEALING

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TABLE-DRIVEN-AGENT

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TREE-CSP-SOLVER

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TREE-SEARCH

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TT-CHECK-ALL

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TT-ENTAILS?

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UNIFORM-COST-SEARCH

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WALKSAT

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HILL-CLIMBING

See pic A heuristic, problem-solving strategy in which each step moves you progressively closer to the final goal. Version: steepest ascent local search algorithm, in which the climber simply moves in the direction of increasing value AKA, greedy local search

GRAPH-SEARCH

See pic Fun fact: Facebook's way of mapping all of the data we give the platform together in a really useful way; best example of 'Social Search' - the premise of creating a search engine based not on websites but on entities - people, places and things

rule if-then

See rule condition-action

rule situation-action

See rule condition-action

partial observability

Seeing the system in its apparent state, not its real state. might have noisy inaccurate sensors, or missing data. Like our local Roomba robot.

search partially observable

Seeing the system in its apparent state, not its real state. might have noisy inaccurate sensors, or missing data. Like our local Roomba robot. Even the most efficient solution algorithm is not of much use when no solutions exist. Many things just cannot be done without sensing. For example, the sensorless 8-puzzle is impossible. On the other hand, a little bit of sensing can go a long way. For example, every 8-puzzle instance is solvable if just one square is visible—the solution involves moving each tile in turn into the visible square and then keeping track of its location.

goal clauses

Slightly more general is the Horn clause, which is a disjunction of literals of which at most one is positive. So all definite clauses are Horn clauses, as are clauses with no positive literals; these are called goal clauses.

Monte Carlo (in games)

Solving even one deal is quite difficult, so solving ten million is out of the question. Instead, we resort to a Monte Carlo approximation: instead of adding up all the deals, we take a random sample of N deals, where the probability of deal s appearing in the sample is proportional to P(s)

neat vs. scruffy

Some have characterized this change as a victory of the neats—those who think that AI theories should be grounded in mathematical rigor—over the scruffies—those who would rather try out lots of ideas, write some programs, and then assess what seems to be working. Both approaches are important. A shift toward neatness implies that the field has reached a level of stability and maturity. Whether that stability will be disrupted by a new scruffy idea is another question.

forward pruning

Some moves at a given node are pruned immediately without further consideration

checkmate accidental

Sometimes a checkmate strategy works for some of the board states in the current belief state but not others. Accidental in the sense that White could not know that it would be checkmate—if Black's pieces happen to be in the right places. (Most checkmates in games between humans are of this accidental nature.)

constraint logic programming (CLP)

Special methods for handling higher-order or global constraints were developed first within the context. Marriott and Stuckey (1998) provide excellent coverage of research in this area.

linear constraint

Special solution algorithms (which we do not discuss here) exist for linear constraints on integer variables—that is, constraints, such as the one just given, in which each variable appears only in linear form.

learning checkers

Starting in 1952, Arthur Samuel wrote a series of programs for checkers (draughts) that eventually learned to play at a strong amateur level. Along the way, he disproved the idea that computers can do only what they are told to: his program quickly learned to play a better game than its creator.

hill climbing stochastic

Stochastic hill climbing chooses at random from among the uphill moves; the probability of selection can vary with the steepness of the uphill move. This usually converges more slowly than steepest ascent, but in some state landscapes, it finds better solutions.

search heuristic

Strategies that know whether one non-goal state is "more promising" than another are called informed search or heuristic search strategies;

k-consistency

Stronger forms of propagation can be defined with the notion of k-consistency. A CSP is k-consistent if, for any set of k − 1 variables and for any consistent assignment to those variables, a consistent value can always be assigned to any kth variable. 1-consistency says that, given the empty set, we can make any set of one variable consistent: this is what we called node consistency. 2-consistency is the same as arc consistency. For binary constraint networks, 3-consistency is the same as path consistency.

environment class

Such experiments are often carried out not for a single environment but for many environments drawn from an environment class

disjunctive constraint

Suppose we have four workers to install wheels, but they have to share one tool that helps put the axle in place. We need a disjunctive constraint to say that AxleF and AxleB must not overlap in time; either one comes first or the other does: (AxleF + 10 ≤ AxleB) or (AxleB + 10 ≤ AxleF ) . This looks like a more complicated constraint, combining arithmetic and logic. But it still reduces to a set of pairs of values that AxleF and AxleF can take on.

machine learning

Technique for making a computer produce better results by learning from past experiences.

chess prediction

Terms such as "visible future" can be interpreted in various ways, but Simon also made more concrete predictions: that within 10 years a computer would be chess champion, and a significant mathematical theorem would be proved by machine. These predictions came true (or approximately true) within 40 years rather than 10. Simon's overconfidence was due to the promising performance of early AI systems on simple examples. In almost all cases, however, these early systems turned out to fail miserably when tried out on wider selections of problems and on more difficult problems.

DEEP THOUGHT

The $10,000 prize for the first program to achieve a USCF (United States Chess Federation) rating of 2500 (near the grandmaster level) was awarded to DEEP THOUGHT (Hsu et al., 1990) in 1989. The grand prize, $100,000, went to DEEP BLUE (Campbell et al., 2002; Hsu, 2004) for its landmark victory over world champion Garry Kasparov in a 1997 exhibition match

15-puzzle

The 8-puzzle is a smaller cousin of the 15-puzzle, whose history is recounted at length by Slocum and Sonneveld (2006). It was widely believed to have been invented by the famous American game designer Sam Loyd, based on his claims to that effect from 1891 onward (Loyd, 1959). Actually it was invented by Noyes Chapman, a postmaster in Canastota, New York, in the mid-1870s. (Chapman was unable to patent his invention, as a generic patent covering sliding blocks with letters, numbers, or pictures was granted to Ernest Kinsey in 1878.) It quickly attracted the attention of the public and of mathematicians (Johnson and Story, 1879; Tait, 1880). The editors of the American Journal of Mathematics stated, "The '15' puzzle for the last few weeks has been prominently before the American public, and may safely be said to have engaged the attention of nine out of ten persons of both sexes and all ages and conditions of the community." Ratner and Warmuth (1986) showed that the general n × n version of the 15-puzzle belongs to the class of NP-complete problems.

problem 8-puzzle

The 8-puzzle was one of the earliest heuristic search problems. As mentioned in Section 3.2, the object of the puzzle is to slide the tiles horizontally or vertically into the empty space until the configuration matches the goal configuration (Figure 3.28).

8-puzzle

The 8-puzzle, an instance of which is shown in Figure 3.4, consists of a 3×3 board with eight numbered tiles and a blank space. A tile adjacent to the blank space can slide into the space. The object is to reach a specified goal state, such as the one shown on the right of the figure. The standard formulation is as follows: • States: A state description specifies the location of each of the eight tiles and the blank in one of the nine squares. • Initial state: Any state can be designated as the initial state. Note that any given goal can be reached from exactly half of the possible initial states (Exercise 3.4). • Actions: The simplest formulation defines the actions as movements of the blank space Left, Right, Up, or Down. Different subsets of these are possible depending on where the blank is. • Transition model: Given a state and action, this returns the resulting state; for example, if we apply Left to the start state in Figure 3.4, the resulting state has the 5 and the blank switched. • Goal test: This checks whether the state matches the goal configuration shown in Figure 3.4. (Other goal configurations are possible.) • Path cost: Each step costs 1, so the path cost is the number of steps in the path.

chronological backtracking

The BACKTRACKING-SEARCH algorithm has a very simple policy for what to do when a branch of the search fails: back up to the preceding variable and try a different value for it. This is called chronological backtracking because the most recent decision point is revisited. In this subsection, we consider better possibilities.

reasoning uncertain

The Bayesian network formalism was invented to allow efficient representation of, and rigorous reasoning with, uncertain knowledge. This approach largely overcomes many problems of the probabilistic reasoning systems of the 1960s and 1970s; it now dominates AI research on uncertain reasoning and expert systems. The approach allows for learning from experience, and it combines the best of classical AI and neural nets.

explanation-based generalization

The GIB program (Ginsberg, 1999) won the 2000 computer bridge championship quite decisively using the Monte Carlo method. Since then, other winning programs have followed GIB's lead. GIB's major innovation is using explanation-based generalization to compute and cache general rules for optimal play in various standard classes of situations rather than evaluating each situation individually.

search real-time

The LRTA∗ algorithm was developed by Korf (1990) as part of an investigation into real-time search for environments in which the agent must act after searching for only a fixed amount of time (a common situation in two-player games).

SSS* algorithm

The SSS∗ algorithm (Stockman, 1979) can be viewed as a two-player A∗ and never expands more nodes than alpha-beta to reach the same decision. The memory requirements and computational overhead of the queue make SSS∗ in its original form impractical, but a linear-space version has been developed from the RBFS algorithm (Korf and Chickering, 1996). Plaat et al. (1996) developed a new view of SSS∗ as a combination of alpha-beta and transposition tables, showing how to overcome the drawbacks of the original algorithm and developing a new variant called MTD(f) that has been adopted by a number of top programs.

validity

The ability of a test to measure what it is intended to measure

ALPHA-BETA-SEARCH

The alpha-beta search algorithm. Notice that these routines are the same as the MINIMAX functions in Figure 5.3, except for the two lines in each of MIN-VALUE and MAX-VALUE that maintain α and β (and the bookkeeping to pass these parameters along).

memory requirements

The amount of memory used by an array depends on the array's data type.

knowledge-based agents

The central component of a knowledge-based agent is its knowledge base

h (heuristic function)

The choice of f determines the search strategy. (For example, as Exercise 3.21 shows, best-first tree search includes depth-first search as a special case.) Most best-first algorithms include as a component of f a heuristic function, denoted h(n): h(n) = estimated cost of the cheapest path from the state at node n to a goal state.

heuristic function

ground resolution theorem

The completeness theorem for resolution in propositional logic is called the ground resolution theorem: If a set of clauses is unsatisfiable, then the resolution closure of those clauses contains the empty clause. This theorem is proved by demonstrating its contrapositive: if the closure RC(S) does not contain the empty clause, then S is satisfiable.

logic resolution

The current section introduces a single inference rule, resolution, that yields a complete inference algorithm when coupled with any complete search algorithm.

convexity

The curvature of the price-yield curve; The more convexity, the worse the duration estimate will differ from actual change

right thing (doing the)

The definitions on the left measure success in terms of fidelity to human performance, whereas the ones on the right measure against an ideal performance measure, called rationality. A system is rational if it does the "right thing," given what it knows.

Diophantine equations

The earliest work related to constraint satisfaction dealt largely with numerical constraints. Equational constraints with integer domains were studied by the Indian mathematician Brahmagupta in the seventh century;

semidynamic environment

The environment itself does not change with the passage of time but the agent's performance score does. the environment doesn't change; however, the time affects the agent's performance criteria score

expected value (in a game tree)

The evaluation function cannot know which states are which, but it can return a single value that reflects the proportion of states with each outcome. Then a reasonable evaluation for states in the category is the expected value.

equivalence (logical)

The first concept is logical equivalence: two sentences α and β are logically equivalent if they are true in the same set of models. We write this as α ≡ β.

heuristic admissible

The first condition we require for optimality is that h(n) be an admissible heuristic. An admissible heuristic is one that never overestimates the cost to reach the goal. Because g(n) is the actual cost to reach n along the current path, and f(n)=g(n) + h(n), we have as an immediate consequence that f(n) never overestimates the true cost of a solution along the current path through n. Admissible heuristics are by nature optimistic because they think the cost of solving the problem is less than it actually is.

search B*

The first nonexhaustive heuristic search algorithm with some theoretical grounding was probably B∗ (Berliner, 1979), which attempts to maintain interval bounds on the possible value of a node in the game tree rather than giving it a single point-valued estimate. Leaf nodes are selected for expansion in an attempt to refine the top-level bounds until one move is "clearly best."

The first successful commercial expert system, R1, began operation at the Digital Equipment Corporation (McDermott, 1982). The program helped configure orders for new computer systems; by 1986, it was saving the company an estimated $40 million a year. By 1988, DEC's AI group had 40 expert systems deployed, with more on the way. DuPont had 100 in use and 500 in development, saving an estimated $10 million a year. Nearly every major U.S. corporation had its own AI group and was either using or investigating expert systems.

taxi automated

The full driving task is extremely open-ended. There is no limit to the novel combinations of circumstances that can arise—another reason we chose it as a focus for discussion. Summarizes the PEAS description for the taxi's task environment. Agent Type: Taxi driver Performance Measure: Safe, fast, legal, comfortable trip, maximize profits Environment: Roads, other traffic, pedestrians, customers Actuators: Steering, accelerator, brake, signal, horn, display Sensors: Cameras, sonar, speedometer, GPS, odometer, accelerometer, engine sensors, keyboard

problem 8-queens

The goal of the 8-queens problem is to place eight queens on a chessboard such that no queen attacks any other. (A queen attacks any piece in the same row, column or diagonal.) Figure 3.5 shows an attempted solution that fails: the queen in the rightmost column is attacked by the queen at the top left.

implementation level

The hardware (circuit boards in computers/ our brains in us) Then we can expect it to cross the Golden Gate Bridge because it knows that that will achieve its goal. Notice that this analysis is independent of how the taxi works at the implementation level. It doesn't matter whether its geographical knowledge is implemented as linked lists or pixel maps, or whether it reasons by manipulating strings of symbols stored in registers or by propagating noisy signals in a network of neurons.

machine evolution

The illusion of unlimited computational power was not confined to problem-solving programs. Early experiments in ____________ (now called genetic algorithms) (Friedberg, 1958; Friedberg et al., 1959) were based on the undoubtedly correct belief that by making an appropriate series of small mutations to a machine-code program, one can generate a program with good performance for any particular task.

Look ma no hands,

The intellectual establishment, by and large, preferred to believe that "a machine can never do X." (See Chapter 26 for a long list of X's gathered by Turing.) AI researchers naturally responded by demonstrating one X after another. John McCarthy referred to this period as the "Look, Ma, no hands!" era.

environment competitive

The key distinction is whether B's behavior is best described as maximizing a performance measure whose value depends on agent A's behavior. For example, in chess, the opponent entity B is trying to maximize its performance measure, which, by the rules of chess, minimizes agent A's performance measure. Thus, chess is a competitive multiagent environment.

critic (in learning)

The learning element uses feedback from the critic on how the agent is doing and determines how the performance element should be modified to do better in the future. part of the learning agent, this component gives feedback from the sensors to the learning element, which in turns adapts the performance element

compositionality

The meaning of a sentence is a function of the meaning of its parts. Propositional logic has a third property that is desirable in representation languages

dynamic backtracking

The method of dynamic backtracking (Ginsberg, 1993) retains successful partial assignments from later subsets of variables when backtracking over an earlier choice that does not invalidate the later success.

search minimax

minimax algorithm

The minimax algorithm (Figure 5.3) computes the minimax decision from the current state. It uses a simple recursive computation of the minimax values of each successor state, directly implementing the defining equations. The recursion proceeds all the way down to the leaves of the tree, and then the minimax values are backed up through the tree as the recursion unwinds. The minimax algorithm performs a complete depth-first exploration of the game tree. If the maximum depth of the tree is m and there are b legal moves at each point, then the time complexity of the minimax algorithm is O(bm). The space complexity is O(bm) for an algorithm that generates all actions at once, or O(m) for an algorithm that generates actions one at a time (see page 87). For real games, of course, the time cost is totally impractical, but this algorithm serves as the basis for the mathematical analysis of games and for more practical algorithms.

missionaries and cannibals

The missionaries and cannibals problem used in Exercise 3.9 was analyzed in detail by Amarel (1968). It had been considered earlier—in AI by Simon and Newell (1961) and in operations research by Bellman and Dreyfus (1962).

chess automaton

The most notorious of these was BaronWolfgang von Kempelen's (1734-1804) "The Turk," a supposed chess-playing automaton that defeated Napoleon before being exposed as a magician's trick cabinet housing a human chess expert (see Levitt, 2000). It played from 1769 to 1854.

IJCAI (International Joint Conference on AI)

The most recent work appears in the proceedings of the major AI conferences: the biennial International Joint Conference on AI (IJCAI), the annual European Conference on AI (ECAI), and the National Conference on AI, more often known as AAAI, after its sponsoring organization.

depth limit

The most straightforward approach to controlling the amount of search is to set a fixed depth limit so that CUTOFF-TEST(state, depth) returns true for all depth greater than some fixed depth d.

search A*

The most widely known form of best-first search is called A∗ search (pronounced "A-star search"). It evaluates nodes by combining g(n), the cost to reach the node, and h(n), the cost to get from the node to the goal: f(n) = g(n) + h(n) . Since g(n) gives the path cost from the start node to node n, and h(n) is the estimated cost of the cheapest path from n to the goal, we have f(n) = estimated cost of the cheapest solution through n .

constraint graph

The nodes of the graph correspond to variables of the problem, and a link connects any two variables that participate in a constraint.

observation prediction

The observation prediction stage determines the set of percepts o that could be observed in the predicted belief state: POSSIBLE-PERCEPTS(ˆ b) = {o : o=PERCEPT(s) and s ∈ ˆ b} .

monotone condition

fixed point

The performing of arithmetical calculations without regard to the position of the radix point. The relative position of the point has to be controlled during calculations.

weak method

The picture of problem solving that had arisen during the first decade of AI research was of a general-purpose search mechanism trying to string together elementary reasoning steps to find complete solutions. Such approaches have been called weak methods because, although general, they do not scale up to large or difficult problem instances. The alternative to weak methods is to use more powerful, domain-specific knowledge that allows larger reasoning steps and can more easily handle typically occurring cases in narrow areas of expertise. One might say that to solve a hard problem, you have to almost know the answer already.

SAT

The problem of determining the satisfiability of sentences in propositional logic—the SAT problem—was the first problem proved to be NP-complete. Many problems in computer science are really satisfiability problems. For example, all the constraint satisfaction problems in Chapter 6 ask whether the constraints are satisfiable by some assignment.

game pursuit-evasion

The problem then becomes a two-player pursuit-evasion game. We assume now that the players take turns moving. The game ends only when the players are on the same node; the terminal payoff to the pursuer is minus the total time taken. (The evader "wins" by never losing.)

The process of looking for a sequence of actions that reaches the goal

legal reasoning

The process of reasoning by which a judge harmonizes his or her decision with the judicial decisions of previous cases.

language translation

The process of writing a verbal/written code into another language.

fitness (in genetic algorithms)

The production of the next generation of states is shown in Figure 4.6(b)-(e). In (b), each state is rated by the objective function, or (in GA terminology) the fitness function. A fitness function should return higher values for better states, so, for the 8-queens problem we use the number of nonattacking pairs of queens, which has a value of 28 for a solution.

tree decomposition

The second approach is based on constructing a tree decomposition of the constraint graph into a set of connected subproblems. Each subproblem is solved independently, and the resulting solutions are then combined. A tree decomposition must satisfy the following three requirements: • Every variable in the original problem appears in at least one of the subproblems. • If two variables are connected by a constraint in the original problem, they must appear together (along with the constraint) in at least one of the subproblems. • If a variable appears in two subproblems in the tree, it must appear in every subproblem along the path connecting those subproblems.

domain finite

The simplest kind of CSP involves variables that have discrete, finite domains. Map-coloring problems and scheduling with time limits are both of this kind.

reflex agent

The simplest kind of agent is the simple reflex agent. These agents select actions on the basis of the current percept, ignoring the rest of the percept history.

search iterative deepening A*

The simplest way to reduce memory requirements for A∗ is to adapt the idea of iterative deepening to the heuristic search context, resulting in the iterative-deepening A∗ (IDA∗) algorithm. The main difference between IDA∗ and standard iterative deepening is that the cutoff used is the f-cost (g+h) rather than the depth; at each iteration, the cutoff value is the smallest f-cost of any node that exceeded the cutoff on the previous iteration. IDA∗ is practical for many problems with unit step costs and avoids the substantial overhead associated with keeping a sorted queue of nodes. Unfortunately, it suffers from the same difficulties with real-valued costs as does the iterative version of uniform-cost search

logic propositional syntax

The syntax of propositional logic defines the allowable sentences. The atomic sentences consist of a single proposition symbol. Each such symbol stands for a proposition that can be true or false. We use symbols that start with an uppercase letter and may contain other letters or subscripts, for example: P, Q, R, W1,3 and North. The names are arbitrary but are often chosen to have some mnemonic value—we use W1,3 to stand for the proposition that the wumpus is in [1,3]. (Remember that symbols such as W1,3 are atomic, i.e., W, 1, and 3 are not meaningful parts of the symbol.) There are two proposition symbols with fixed meanings: True is the always-true proposition and False is the always-false proposition. Complex sentences are constructed from simpler sentences, using parentheses and logical connectives.

temporal projection

The temporal-projection problem, which involves determining what propositions hold true after an action sequence is executed, can be seen as a special case of state estimation with empty percepts.

quadratic dynamical systems

The theory of quadratic dynamical systems may also explain the performance of GAs (Rabani et al., 1998). See Lohn et al. (2001) for an example of GAs applied to antenna design, and Renner and Ekart (2003) for an application to computer-aided design.

search parallel

The topic of parallel search algorithms was not covered in the chapter, partly because it requires a lengthy discussion of parallel computer architectures. Parallel search became a popular topic in the 1990s in both AI and theoretical computer science (Mahanti and Daniels, 1993; Grama and Kumar, 1995; Crauser et al., 1998) and is making a comeback in the era of new multicore and cluster architectures (Ralphs et al., 2004; Korf and Schultze, 2005). Also of increasing importance are search algorithms for very large graphs that require disk storage (Korf, 2008).

problem traveling salesperson

The traveling salesperson problem (TSP) is a touring problem in which each city must be visited exactly once. The aim is to find the shortest tour. The problem is known to be NP-hard, but an enormous amount of effort has been expended to improve the capabilities of TSP algorithms. In addition to planning trips for traveling salespersons, these algorithms have been used for tasks such as planning movements of automatic circuit-board drills and of stocking machines on shop floors.

wumpus world

The wumpus world is a cave consisting of rooms connected by passageways. Lurking somewhere in the cave is the terrible wumpus, a beast that eats anyone who enters its room. The wumpus can be shot by an agent, but the agent has only one arrow. Some rooms contain bottomless pits that will trap anyone who wanders into these rooms (except for the wumpus, which is too big to fall in). The only mitigating feature of this bleak environment is the possibility of finding a heap of gold. Although the wumpus world is rather tame by modern computer game standards, it illustrates some important points about intelligence.

dynamic weighting

There are many variations on the A∗ algorithm. Pohl (1973) proposed the use of dynamic weighting, which uses a weighted sum fw(n)=wgg(n) + whh(n) of the current path length and the heuristic function as an evaluation function, rather than the simple sum f(n)=g(n)+h(n) used in A∗. The weights wg and wh are adjusted dynamically as the search progresses. Pohl's algorithm can be shown to be -admissible—that is, guaranteed to find solutions within a factor 1 + E of the optimal solution, where E is a parameter supplied to the algorithm

SIMPLE-REFLEX-AGENT

These agents select actions on the basis of the current percept, ignoring the rest of the percept history. agents that determine an action based on the current precept. Does not use a precept sequence See pic

game theory combinatorial

These techniques decompose a position into sub-positions that can be analyzed separately and then combined (Berlekamp and Wolfe, 1994; M¨uller, 2003). The optimal solutions obtained in this way have surprised many professional Go players, who thought they had been playing optimally all along. Current Go programs play at the master level on a reduced 9 × 9 board, but are still at advanced amateur level on a full board.

Dartmouth workshop

They organized a two-month workshop at Dartmouth in the summer of 1956. The proposal states: We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.

thought laws of

Thinking rationally. The Greek philosopher Aristotle was one of the first to attempt to codify "right thinking," that is, irrefutable reasoning processes.

heuristic composite

This composite heuristic uses whichever function is most accurate on the node in question. Because the component heuristics are admissible, h is admissible; it is also easy to prove that h is consistent. Furthermore, h dominates all of its component heuristics.

Perceptron convergence theorem

This theorem says that the learning algorithm can adjust the connection strengths of a perceptron to match any input data, provided such a match exists.

interleaving (search and action)

This type of interleaving of search and execution is also useful for exploration problems (see Section 4.5) and for game playing (see Chapter 5).

problem missionaries and cannibals

Three missionaries and three cannibals are on one side of a river, along with a boat that can hold one or two people. Find a way to get everyone to the other side without ever leaving a group of missionaries in one place outnumbered by the cannibals in that place. This problem is famous in AI because it was the subject of the first paper that approached problem formulation from an analytical viewpoint (Amarel, 1968).

precondition axiom

To avoid generating plans with illegal actions, we must add precondition axioms stating that an action occurrence requires the preconditions to be satisfied

resolution closure

To conclude our discussion of resolution, we now show why PL-RESOLUTION is complete. To do this, we introduce the resolution closure RC(S) of a set of clauses S, which is the set of all clauses derivable by repeated application of the resolution rule to clauses in S or their derivatives.

problem touring

Touring problems are closely related to route-finding problems, but with an important difference. Consider, for example, the problem "Visit every city in Figure 3.2 at least once, starting and ending in Bucharest." As with route finding, the actions correspond to trips between adjacent cities. The state space, however, is quite different. Each state must include not just the current location but also the set of cities the agent has visited. So the initial state would be In(Bucharest ), visited({Bucharest}), a typical intermediate state would be In(Vaslui ), Visited({Bucharest , Urziceni , Vaslui}), and the goal test would check whether the agent is in Bucharest and all 20 cities have been visited.

simulated annealing

Uses a temperature parameter to control the probability of downward steps, high temperature equates with a high chance of trying locally bad moves; temperature begins high and gradually decreases over time based on a schedule. Imagine a ping pong ball trying to get to the global minimum. We shake hard first (T is very high) to bump it out of local maxima. As the temperature decreases, the shaking is lowered. Implemented by randomly choosing next move. If next move is less, go. If more, then prob is related to temperature and how much worse it is.

weighted linear function

Vector dot product of a weight vector and a state feature vector

search hill-climbing

Version: steepest ascent local search algorithm, in which the climber simply moves in the direction of increasing value AKA, greedy local search

production

We call such a connection a condition-action rule,5 written as if car-in-front-is-braking then initiate-braking. Also called situation-action rules, productions, or if-then rules.

label (in plans)

We can express this solution by adding a label to denote some portion of the plan and using that label later instead of repeating the plan itself

logic propositional

We cover the syntax of propositional logic and its semantics—the way in which the truth of sentences is determined. Then we look at entailment—the relation between a sentence and another sentence that follows from it—and see how this leads to a simple algorithm for logical inference. Everything takes place, of course, in the wumpus world. The syntax of propositional logic defines the allowable sentences. The atomic sentences consist of a single proposition symbol.

learning to search

We have presented several fixed strategies—breadth-first, greedy best-first, and so on—that have been designed by computer scientists. Could an agent learn how to search better? The answer is yes, and the method rests on an important concept called the metalevel state space.

qualification problem

We said that the Forward action moves the agent ahead unless there is a wall in the way, but there are many other unusual exceptions that could cause the action to fail: the agent might trip and fall, be stricken with a heart attack, be carried away by giant bats, etc.

path loopy

We say that In(Arad) is a repeated state in the search tree, generated in this case by a loopy path. Considering such loopy paths means that the complete search tree for Romania is infinite because there is no limit to how often one can traverse a loop.

constraint symmetry-breaking

We would like to reduce the search space by a factor of n! by breaking the symmetry. This constraint ensures that only one of the n! solutions is possible

symmetry breaking (in CSPs)

We would like to reduce the search space by a factor of n! by breaking the symmetry. We do this by introducing a symmetry-breaking constraint. For our example, we might impose an arbitrary ordering constraint, NT < SA < WA, that requires the three values to be in alphabetical order. This constraint ensures that only one of the n! solutions is possible: {NT = blue,SA = green,WA = red}.

language understanding

Wernicke's area in the temporal lobe of the cerebrum

logical piano

William Stanley Jevons, one of those who improved upon and extended Boole's work, constructed his "logical piano" in 1869 to perform inferences in Boolean logic. An entertaining and instructive history of these and other early mechanical devices for reasoning is given by Martin Gardner (1968).

domain infinite

With infinite domains, it is no longer possible to describe constraints by enumerating all allowed combinations of values.

constraint language

With infinite domains, it is no longer possible to describe constraints by enumerating all allowed combinations of values. Instead, a constraint language must be used that understands constraints such as T1 + d1 ≤ T2 directly, without enumerating the set of pairs of allowable values for (T1, T2).

contradiction

a combination of statements, ideas, or features of a situation that are opposed to one another.

table tennis

a game in which two or four people hit a small ball over a low net on a large table

tic-tac-toe

a game of X and O

problem

a gap between a desired state and an existing state

ply

a half-move. A full move would be my move and the opponent's move

transposition table

a hash table of transpositions, so that they don't have to be explored again

Maximum local

a local maximum is a peak that is higher than each of its neighboring states but lower than the global maximum. Hill-climbing algorithms that reach the vicinity of a local maximum will be drawn upward toward the peak but will then be stuck with nowhere else to go.

higher-order logic

a logic that is more powerful than first-order predicate calculus, e.g., one that allows quantification over predicate symbols.

syllogism

a logical structure that uses the major premise and minor premise to reach a necessary conclusion. Aristotle's syllogisms were what we would now call inference rules. Although the syllogisms included elements of both propositional and first-order logic, the system as a whole lacked the compositional properties required to handle sentences of arbitrary complexity.

simplex algorithm

a method used to solve linear programming problems

search backtracking

a modification to DFS, in which we modify the node in memory and revert the node if a solution is not found. Has O(m) memory, as we need to keep track of the up to m actions performed on the node

neuron

a nerve cell; the basic building block of the nervous system

expectiminimax complexity of

a new minimax function, which incorporates a chance player that is the sum of s and r

nondeterministic environment

a nondeterministic environment is one in which actions are characterized by their possible outcomes, but no probabilities are attached to them. Nondeterministic environment descriptions are usually associated with performance measures that require the agent to succeed for all possible outcomes of its actions.

strategy

a plan of action; contingency plan

shoulder (in state space)

a plateau is a flat area of the state-space landscape. It can be a flat local maximum, from which no uphill exit exists, or a shoulder, from which progress is possible.

premise

a previous statement or proposition from which another is inferred or follows as a conclusion.

optimism under uncertainty

a principle of LRTA* actions that have not been tried in a state s are assumed to lead to goal state

subproblem

a problem that is a subset of another problem

queue priority

a queue that pops the element with the highest priority

search tree

a representation of all the possible moves branching out from the initial state of the problem. a tree that is superimposed on the full game tree and examines enough node to determine where to move. root node is initial state, nodes are possible states and edges are actions to reach those states.

ridge (in local search)

a sequence of local maximums that make greedy search hard to go throuh

forward chaining

a series of "if-then-else" condition pairs is performed The forward-chaining algorithm PL-FC-ENTAILS?(KB, q) determines if a single proposition symbol q—the query—is entailed by a knowledge base of definite clauses. It begins from known facts (positive literals) in the knowledge base. If all the premises of an implication are known, then its conclusion is added to the set of known facts.

canonical form

a standardized form of expressions or data. If all programs put their expressions into a canonical form, the number of cases that will have to be considered by other programs is reduced.

rule

a statement of relation between concepts. Implications are also known as rules or if-then statements. The implication symbol is sometimes written in other books as ⊃ or →.

locality

a surrounding or nearby region. finding out where a robot is, given a map of the world and a sequence of percepts and actions

fact

a thing that is indisputably the case.

exploration

a type of information gathering in which an agent performs a series of action to get information in a "partially-observable" environment Problem: a problem in which the agent must use its actions as experiments in order to learn

connectionism

a type of information-processing approach that emphasizes the simultaneous activity of numerous interconnected processing units

microworld

a type of simulation software that allows learners to manipulate, explore, and experiment with specific phenomena in an exploratory learning environment. Minsky supervised a series of students who chose limited problems that appeared to require intelligence to solve. These limited domains became known as microworlds.

variable ordering

a variable ordering that chooses a variable with the minimum number of remaining values helps minimize the number of nodes in the search tree by pruning larger parts of the tree earlier.

line search

a way of adjusting the step size so that we could converge rapidly without overstepping. Way it does this is by doubling step size in the gradient direction until f is decreasing again. The basic problem is that, if α is too small, too many steps are needed; if α is too large, the search could overshoot the maximum. The technique of line search tries to overcome this dilemma by extending the current gradient direction—usually by repeatedly doubling α—until f starts to decrease again. The point at which this occurs becomes the new current state. There are several schools of thought about how the new direction should be chosen at this point.

sideways move (in state space)

a way to combat shoulders. If all neighbors are equal height, then we should keep exploring

discretization

a way to make a continuous space into a __ space, by adding a small delta value as the neighboring node. If k variables, there will be 2k neighbors for each point (correlating to +- the delta value on each variable) Divide the range of a continuous attribute into intervals. For example, we can move only one airport at a time in either the x or y direction by a fixed amount ±δ. With 6 variables, this gives 12 possible successors for each state. We can then apply any of the local search algorithms described previously. We could also apply stochastic hill climbing and simulated annealing directly, without discretizing the space. These algorithms choose successors randomly, which can be done by generating random vectors of length δ.

search incremental belief-state

a way to solve non-observable environments, in which we solve one initial state and proceed to the next one. If it doesn't, it backtracks, finds another solution, proceeds. It does until it finds a solution that works for all the states.

checkmate guaranteed

a winning strategy, or guaranteed checkmate, is one that, for each possible percept sequence, leads to an actual checkmate for every possible board state in the current belief state, regardless of how the opponent moves. With this definition, the opponent's belief state is irrelevant—the strategy has to work even if the opponent can see all the pieces. This greatly simplifies the computation.

logical connective

a word, such as "and," "or," or "not," that is used to combine or alter statements to create new statements

rationality limited

acting appropriately when there is not enough time to do all the computations one might like.

survey propagation

algorithms such as survey propagation (Parisi and Zecchina, 2002; Maneva et al., 2007) take advantage of special properties of random SAT instances near the satisfiability threshold and greatly outperform general SAT solvers on such instances.

frontier

all of the unexpanded leaf nodes form this. Usually this is stored in some sort of stack, queue, or priority queue AKA: open list

game of perfect information

all players know the entire history of the game when it is their turn to move

game dice

all the "dice" are rolled at the beginning.

reasoning logical

all the different logic options. Logical reasoning should ensure that the new configurations represent aspects of the world that actually follow from the aspects that the old configurations represent.

percept sequence

an agent's input at a given instance. a history of inputs that the agent has perceived.

constraint propagation

an algorithm can search (choose a new variable assignment from several possibilities) or do a specific type of inference

Graph coloring

an algorithm for assigning a minimal number of ``colors'' to nodes of a graph such that no two nodes that are connected have the same color. Used in compilers as a method of register assignment: colors correspond to registers, nodes to variables or def-use chains, and connections to variables that are simultaneously live.

genetic algorithm

an artificial intelligence system that mimics the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem. like stochastic beam search, except instead of choosing k nodes asexually, k nodes are chosen as population, and sexually combine to form the k new start nodes on the weight of their fitness functions the genetic mating occurs through crossovers between two substring representation of the

checkmate probabilistic

an entirely new concept that makes no sense in fully observable games: probabilistic checkmate. Such checkmates are still required to work in every board state in the belief state; they are probabilistic with respect to randomization of the winning player's moves.

environment fully observable

an environment in which the agent knows the complete relevant state of the environment at all times. No need for an internal state or exploration If an agent's sensors give it access to the complete state of the environment at each point in time. A task environment is effectively fully observable if the sensors detect all aspects that are relevant to the choice of action; relevance, in turn, depends on the performance measure. Fully observable environments are convenient because the agent need not maintain any internal state to keep track of the world

truth

any length of the side of a triangle is less than the sum of the length of the other two sides A way to think of a consistent heuristic, with points of the triangle being n, n', and the goal state

successor-state axiom

any state reachable by the current state through an action

Heuristic Programming Project (HPP)

approach—the clean separation of the knowledge (in the form of rules) from the reasoning component. With this lesson in mind, Feigenbaum and others at Stanford began the Heuristic Programming Project (HPP) to investigate the extent to which the new methodology of expert systems could be applied to other areas of human expertise.

rationalism

belief in reason and logic as the primary source of knowledge

taxi

cab

resolvent

causing solution; causing a resolution. Now comes the first application of the resolution rule: the literal ¬P2,2 in R13 resolves with the literal P2,2 in R15 to give the resolvent

soma

cell body

satisficing

choosing a "good enough" alternative

completeness of inference

co-NP-complete so every known inference algorithm for propositional logic has a worst-case complexity that is exponential in the size of the input.

real-world problem

compared to a toy-problem, is one whose solutions people actually care about

Logic Theorist

computer program devised by Alan Newell and Herbert Simon that was able to solve logic problems

offline search

computing a complete solution before setting and executing the solution

Random-restart hill climbing

conduct a series of hill-climbing searches from randomly generated initial states

wiggly belief state

conservative approximation to the exact (wiggly) belief state

operations research

consists of mathematical model building and other applications of quantitative techniques to managerial problems. For the most part, economists did not address the third question listed above, namely, how to make rational decisions when payoffs from actions are not immediate but instead result from several actions taken in sequence. This topic was pursued in the field of operations research, which emerged in World War II from efforts in Britain to optimize radar installations, and later found civilian applications in complex management decisions.

caching

copying information into faster storage system; main memory can be viewed as a cache for secondary storage. The local storage of frequently needed files that would otherwise be obtained from an external source.

environment generator

could be a state generator, to generate possible next states from a current state. Book defines though as a way to train agents by throwing them into a particular simulated environment.

logic programming

declares what outcome the program should accomplish

DEPTH-LIMITED-SEARCH

depth-first search with a specified depth limit, call it l Is not complete Is not optimal TC: b ^ l SC: bl

search depth-limited

depth-first search with a specified depth limit, call it l Is not complete Is not optimal TC: b ^ l SC: bl

problem frame

description changes decision. The information has been lost because the effect axiom fails to state what remains unchanged as the result of an action. The name "frame problem" comes from "frame of reference" in physics—the assumed stationary background with respect to which motion is measured. It also has an analogy to the frames of a movie, in which normally most of the background stays constant while changes occur in the foreground.

model transition

description of what each action does; the formal name for this is the transition model, specified by a function RESULT(s, a) that returns the state that results from doing action a in state s.

object-oriented programming

designing a program by discovering objects, their properties, and their relationships

vacuum world

example—the vacuum-cleaner world shown in Figure 2.2. This world is so simple that we can describe everything that happens; it's also a made-up world, so we can invent many variations. This particular world has just two locations: squares A and B. The vacuum agent perceives which square it is in and whether there is dirt in the square. It can choose to move left, move right, suck up the dirt, or do nothing. One very simple agent function is the following: if the current square is dirty, then suck; otherwise, move to the other square. A partial tabulation of this agent function is shown in Figure 2.3 and an agent program that implements it appears in Figure 2.8 on page 48.

search uniform-cost

expand the tree using a priority queue on order of its step cost Is complete Is always optimal TC = SC: O( b ^ (1 + C* / e) )

uniform-cost search

expand the tree using a priority queue on order of its step cost Is complete Is always optimal TC = SC: O( b ^ (1 + C* / e) )

problem solving

finding a way around an obstacle to reach a goal. The picture of problem solving that had arisen during the first decade of AI research was of a general-purpose search mechanism trying to string together elementary reasoning steps to find complete solutions. Such approaches have been called weak methods because, although general, they do not scale up to large or difficult problem instances. The alternative to weak methods is to use more powerful, domain-specific knowledge that allows larger reasoning steps and can more easily handle typically occurring cases in narrow areas of expertise. One might say that to solve a hard problem, you have to almost know the answer already.

goal formulation of

for a problem-solving agent, determine what the desired goal is

pattern database

for heuristics, we can store subproblems in this type of database, in which we know the solution and step cost. This way, our heuristics become more accurate and faster to compute

chance node (game tree)

for stochastic games (games that combine luck and skill), the minimax must include these nodes, drawn in circles.Each {0} has an expected value based on its probability function A game tree in backgammon must include chance nodes in addition to MAX and MIN nodes. Chance nodes are shown as circles in Figure 5.11. The branches leading from each chance node denote the possible dice rolls; each branch is labeled with the roll and its probability.

heuristic Manhattan

h2 = the sum of the distances of the tiles from their goal positions. Because tiles cannot move along diagonals, the distance we will count is the sum of the horizontal and vertical distances. This is sometimes called the city block distance or Manhattan distance. h2 is also admissible because all any move can do is move one tile one step closer to the goal. Tiles 1 to 8 in the start state give a Manhattan distance of h2 = 3+1 + 2 + 2+ 2 + 3+ 3 + 2 = 18 . As expected, neither of these overestimates the true solution cost, which is 26.

procedural approach

holds that rules should be clearly stated and consistently and impartially enforced. In contrast, the procedural approach encodes desired behaviors directly as program code. In the 1970s and 1980s, advocates of the two approaches engaged in heated debates.

search strategy

how to determine which node to expand next in a search tree

search cost

how to evaluate the effectiveness of an algorithm: this is the algorithm's time complexity + (maybe memory complexity)

total cost

how to evaluate the effectiveness of an algorithm: this is the search cost + the path cost of the solution found

consistency arc

if every value in its domain satisfies the variable's binary constraints.

environment uncertain

if it is not fully observable or not deterministic.

deterministic environment

if the agent's actions have predictable effects. Ie, given a current state and the agent's action, we could predict the next state

dynamic environment

if the environment can change while the agent is deliberating. Ex: an actual Roomba room.

search simulated annealing

imagine a ping pong ball trying to get to the global minimum. We shake hard first (T is very high) to bump it out of local maxima. As the temperature decreases, the shaking is lowered. Implemented by randomly choosing next move. If next move is less, go. If more, then prob is related to temperature and how much worse it is.

constraint resource constraint

important higher-order constraint is the resource constraint, sometimes called the atmost constraint. The constraint that no more than 10 personnel are assigned in total is written as Atmost(10, P1, P2, P3, P4).

undecidability

impossibility of deciding between competing interpretations

schema (in a genetic algorithm)

in a genetic algorithm, this refers to the substring in which some portions are unspecified. String that match this schema are called instances Schemas that have a high fitness functions will become more populous

step size

in a gradient ascent / gradient descent algorithm, this is the size of alpha (multiply by gradient to find Δx)

randomization

in order to prevent infinite loops, a simple reflex agent might use this in order to pick a next action

Representation structured

in which objects such as cows and trucks and their various and varying relationships can be described explicitly. (See Figure 2.16(c).) Structured representations underlie relational databases and first-order logic (Chapters 8, 9, and 12), first-order probability models (Chapter 14), knowledge-based learning (Chapter 19) and much of natural language understanding (Chapters 22 and 23). In fact, almost everything that humans express in natural language concerns objects and their relationships.

quiescence

inactivity; stillness; motionlessness; quality of being at rest. unlikely to exhibit wild swings in value in the near future.

search quiescence

inactivity; stillness; motionlessness; quality of being at rest. unlikely to exhibit wild swings in value in the near future Nonquiescent positions can be expanded further until quiescent positions are reached. This extra search is called a quiescence search; sometimes it is restricted to consider only certain types of moves, such as capture moves, that will quickly resolve the uncertainties in the position.

cutoff test

instead of the terminal test, if using a evaluation an evaluation function, we use this test that checks if we've reached the maximum depth

evaluation function

instead of traversing all the down a minimax tree, cut the search off early and use a heuristic this. This turns nonterminal nodes into terminal leaves

heuristic minimum remaining values

intuitive idea—choosing the variable with the fewest "legal" values—is called the minimum-remaining-values (MRV) heuristic. It also has been called the "most constrained variable" or "fail-first" heuristic, the latter because it picks a variable that is most likely to cause a failure soon, thereby pruning the search tree. If some variable X has no legal values left, the MRV heuristic will select X and failure will be detected immediately—avoiding pointless searches through other variables. The MRV heuristic usually performs better than a random or static ordering, sometimes by a factor of 1,000 or more, although the results vary widely depending on the problem

heuristic minimum-remaining-values

minimum spanning tree (MST)

is a spanning tree such that the total length of its arcs is as small as possible. (MST is sometimes called a minimum connector.) A spanning tree with the smallest possible weight

gradient descent

is an algorithm to improve a hypothesis function in respect to some cost function. opposite of hill climbing, find the minimization of a function

utility function

is essentially an internalization of the performance measure. a formula for calculating the total utility that a particular person derives from consuming a combination of goods and services

environment nondeterministic

is one in which actions are characterized by their possible outcomes, but no probabilities are attached to them. Nondeterministic environment descriptions are usually associated with performance measures that require the agent to succeed for all possible outcomes of its actions.

constraint learning

is the idea of finding a minimum set of variables from the conflict set that causes the problem.

clairvoyance

it assumes that the game will become observable to both players immediately after the first move. Despite its intuitive appeal, the strategy can lead one astray.

independent subproblems

it is obvious that coloring Tasmania and coloring the mainland are independent subproblems-any solution for the mainland combined with any solution for Tasmania yields a solution for the whole map. Independence can be ascertained simply by finding connected components of the constraint graph.

knowledge background

it takes a percept as input and returns an action. The agent maintains a knowledge base, KB, which may initially contain some background knowledge

conclusion (of an implication)

its conclusion or consequence is ¬W(2,2)

toy problem

just a useful contrived example to illustrate AI problems

search local beam

k parallel threads that run search in their areas to generate successors. The k best are chosen for the next run Unlike random restart, the k states talk to each other, and tell unfruitful nodes to come to them.

local beam search

k parallel threads that run search in their areas to generate successors. The k best are chosen for the next run. Unlike random restart, the k states talk to each other, and tell unfruitful nodes to come to them.

learning heuristics

learn from experience. "Experience" here means solving lots of 8-puzzles, for instance. Each optimal solution to an 8-puzzle problem provides examples from which h(n) can be learned. Each example consists of a state from the solution path and the actual cost of the solution from that point. From these examples, a learning algorithm can be used to construct a function h(n) that can (with luck) predict solution costs for other states that arise during search.

LRTA*

learning real-time A*, an exploring algorithm with memory

search stochastic beam

like stochastic search, this is a variation of local beam search in which the next k chosen nodes is chosen randomly based on their fitness

monotonicity of a logical system

logical systems MONOTONICITY is monotonicity, which says that the set of entailed sentences can only increase as information is added to the knowledge base.8 For any sentences α and β, if KB |= α then KB ∧ β |= α .

distributed constraint satisfaction

looks at solving CSPs when there is a collection of agents, each of which controls a subset of the constraint variables

monitoring

maintaining one's belief state at any time AKA filtering, state estimation

incremental formulation

operators that augment the state description Ex: 8-queens problem, but add queens one by one in such that none of the queens are attacked

sequential environment

opposite of episodic ex: chess and taxi driving

performance element

part of a learning system, this adaptive component of the learning agent takes sensor data and turns them into actions

uninformed search

search strategies that have no additional information about the states provided beyond the problem definition AKA blind search algorithms that know nothing more about the problem other than its definition Ie, blind expands nodes, bfs/dfs/itt/depth limited

scruffy vs. neat

see neat vs scruffy

co-NP-complete

so every known inference algorithm for propositional logic has a worst-case complexity that is exponential in the size of the input.

exploration safe

some goal state is reachable from every reachable state.

rule condition-action

some processing is done on the visual input to establish the condition we call "The car in front is braking." Then, this triggers some established connection in the agent program to the action "initiate braking." We call such a connection a condition-action rule, written as if car-in-front-is-braking then initiate-braking. Also called situation-action rules, productions, or if-then rules.

softbot

some software agents (or software robots or softbots) exist in rich, unlimited domains.

predecessor

someone or something that came before another

needle in a haystack

something hard or impossible to find. In understanding entailment and inference, it might help to think of the set of all consequences of KB as a haystack and of α as a needle. Entailment is like the needle being in the haystack; inference is like finding it. This distinction is embodied in some formal notation: if an inference algorithm i can derive α from KB, we write KB i α , which is pronounced "α is derived from KB by i" or "i derives α from KB."

entailment

something involved as a necessary part or consequence of something

search informed

strategies that know when one goal state might be more promising than the other AKA heuristic search

physical symbol system

symbols are physical patterns that can be combined to form complex symbol structures. contain process for manipulating complex symbol structures. The success of GPS and subsequent programs as models of cognition led Newell and Simon (1976) to formulate the famous physical symbol system hypothesis, which states that "a physical symbol system has the necessary and sufficient means for general intelligent action."

connective logical

that any computable function could be computed by some network of connected neurons, and that all the logical connectives (and, or, not, etc.) could be implemented by simple net structures.

search general

the agent can construct sequences of actions that achieve its goals;

environment unknown

the agent will have to learn how it works in order to make good decisions.

expressiveness (of a representation scheme)

the axis along which atomic, factored, and structured representations lie is the axis of increasing expressiveness. Roughly speaking, a more expressive representation can capture, at least as concisely, everything a less expressive one can capture, plus some more. Often, the more expressive language is much more concise

heuristic degree

the degree heuristic comes in handy. It attempts to reduce the branching factor on future choices by selecting the variable that is involved in the largest number of constraints on other unassigned variables

rational thought

the exercise of using reason and logic; or demonstrating the use of reason and logic.

General Problem Solver

the first genuine computer simulation of problem-solving behavior

objective function

the goal of an optimization function. Ex: reproductive fitness in nature. the expression that defines the quantity to be maximized or minimized in a linear programming model

control theory

the idea that two control systems- inner controls and outer controls- work against our tendencies to deviate

stack

the last-in, first-out or LIFO queue (also known as a stack)

game zero-sum

the most common games are of a rather specialized kind—what game theorists call deterministic, turn-taking, two-player, zero-sum games of perfect information (such as chess). In our terminology, this means deterministic, fully observable environments in which two agents act alternately and in which the utility values at the end of the game are always equal and opposite.

min-conflicts heuristic

the most obvious heuristic is to select the value that results in the minimum number of conflicts with other variables. Min-conflicts is surprisingly effective for many CSPs.

gradient empirical

the objective function might not be available in a differentiable form at all—for example, the value of a particular set of airport locations might be determined by running some large-scale economic simulation package. In those cases, we can calculate a so-called empirical gradient by evaluating the response to small increments and decrements in each coordinate. Empirical gradient search is the same as steepest-ascent hill climbing in a discretized version of the state space.

stochastic environment

the opposite of deterministic. Ex: taxi driving. Might have erratic traffic, action might not lead to expected consequences.

negation

the opposite of the original statement. ¬ (not). A sentence such as ¬W1,3 is called the negation of W1,3.

environment known

the outcomes (or outcome probabilities if the environment is stochastic) for all actions are given.

inferential frame problem

the problem of projecting forward the results of a t step plan of action in time O(kt) rather than O(nt).

data mining

the process of analyzing data to extract information not offered by the raw data alone

planning

the process of anticipating future events and determining strategies to achieve organizational objectives in the future

cognitive modeling

the process of performing a demonstration combined with verbalizing the thinking behind the actions

contingency planning

the process of preparing alternative courses of action that may be used if the primary plans don't achieve the organization's objectives for a nondeterministic problem, it is if-then sequence of actions that solve the system AKA strategy

induction

the process that moves from a given series of specifics to a generalization. that general rules are acquired by exposure to repeated associations between their elements.

combinatorial explosion

the rapid expansion of resources required to encode configurations as the number of component features increases

relative error

the ratio of absolute error to actual measure. For problems with constant step costs, the growth in run time as a function of the optimal solution depth d is analyzed in terms of the the absolute error or the relative error of the heuristic.

competitive ratio

the ratio of total path cost that an exploring agent traveled : the total path cost if we knew the environment in advance

satisfiability threshold conjecture

the satisfiability threshold conjecture says that for every k ≥ 3, there is a threshold ratio rk such that, as n goes to infinity, the probability that CNFk(n, rn) is satisfiable becomes 1 for all values of r below the threshold, and 0 for all values above.

cognitive psychology

the scientific study of all the mental activities associated with thinking, knowing, remembering, and communicating

linguistics

the scientific study of the structure, sounds, and meaning of language

shortest path

the shortest path between a start node and a goal node in a weighted graph. <

optimal solution

the single best solution to a problem. a solution minimizes the path cost.

problem formulation

the stage of creative behavior that involves identifying a problem or opportunity requiring a solution that is as yet unknown decide what actions and states to consider, given a goal

object-level state space

the state space of a standard search problem, like route finding.

cybernetics

the study of information processing, feedback, and control in communication systems

city block distance

the sum of the distances of the tiles from their goal positions. Because tiles cannot move along diagonals, the distance we will count is the sum of the horizontal and vertical distances. This is sometimes called the city block distance or Manhattan distance. h2 is also admissible because all any move can do is move one tile one step closer to the goal.

minimax value

the value of the move, being the defined by the minimax function Minimax algorithm is depth first

material value

the value/reward/cost of an item introductory chess books give an approximate material value for each piece: each pawn is worth 1, a knight or bishop is worth 3, a rook 5, and the queen 9. Other features such as "good pawn structure" and "king safety" might be worth half a pawn, say. These feature values are then simply added up to obtain the evaluation of the position.

gradient

the vector of the partial derivative of the objective function with respect to each variable. Gives the magnitude and direction of the steepest increase Then, can find the maximum by setting to 0, or using gradient ascent / descent algorithm

horizon effect

there's an unavoidable move that causes serious damage, but we could temporarily avoid it. So it does stupid moves, due to an early cutoff test. The horizon effect is more difficult to eliminate. It arises when the program is facing an opponent's move that causes serious damage and is ultimately unavoidable, but can be temporarily avoided by delaying tactics.

consistency path

tightens the binary constraints by using implicit constraints that are inferred by looking at triples of variables.

traveling salesperson problem (TSP)

touring problem, with aim of finding shortest tour that visits every city

TSP

traveling salesperson problem

search breadth-first

traverse the tree using a FIFO queue. Is complete Is optimal if path cost is non-decreasing TC: O(b^d) SC: O(b^d)

depth-first search (plus, what are it's problem-solving performance measures?)

traverse the tree using a LIFO queue (aka stack) Is complete in finite state spaces, as long as there is redundancy checks. Otherwise, incomplete Non optimal TC: O(b^d) SC: O(bm) Where m is the maximum length any path in the state space

search depth-first

traverse the tree using a LIFO queue (aka stack)Is complete in finite state spaces, as long as there is redundancy checks. Otherwise, incomplete Non optimal TC: O(b^d) SC: O(bm) Where m is the maximum length any path in the state space

language processing

understanding language and producing it

structured representation

unlike factored representations, these representation stores relationships between a set of variables as well.

search in a CSP

use backtracking search

online search

uses interleaving, takes action, observes the environment, and computes the next action

search online

uses interleaving, takes action, observes the environment, and computes the next action

pattern database disjoint

we record not the total cost of solving the 1-2-3-4 subproblem, but just the number of moves involving 1-2-3-4. Then it is easy to see that the sum of the two costs is still a lower bound on the cost of solving the entire problem. This is the idea behind disjoint pattern databases. With such databases, it is possible to solve random 15-puzzles in a few milliseconds—the number of nodes generated is reduced by a factor of 10,000 compared with the use of Manhattan distance.

commitment ontological

what it assumes about the nature of reality.

search alpha-beta

when applied to a minimax, helps to prune away game states that will never be played alpha = highest-value choice we've found so far beta = lowest-value we've found so far

convex optimization

which allows the constraint region to be any convex region and the objective to be any function that is convex within the constraint region

futility pruning

which helps decide in advance which moves will cause a beta cutoff in the successor nodes.

pruning futility

which helps decide in advance which moves will cause a beta cutoff in the successor nodes.

dead end

which no goal state is reachable. if the environment is irreversible, an agent might reach this, in which no goal state is reachable.

triangle inequality

which stipulates that each side of a triangle cannot be longer than the sum of the other two sides.

deduction theorem

which was known to the ancient Greeks: For any sentences α and β, α |= β if and only if the sentence (α ⇒ β) is valid.

autonomic computing

A self-managing computing model named after, and patterned on, the human body's autonomic nervous system. Effort to develop systems that can manage themselves without user intervention

algorithm

A step-by-step procedure for solving a problem, especially by a computer.

arc consistency

All values in a variable's domain satisfy its binary constraints A variable in a CSP is arc-consistent if every value in its domain satisfies the variable's binary constraints.

alpha-beta search

Alpha-beta search updates the values of α and β as it goes along and prunes the remaining branches at a node (i.e., terminates the recursive call) as soon as the value of the current node is known to be worse than the current α or β value for MAX or MIN, respectively.

atom

An atomic sentence (or atom for short) is formed from a predicate symbol optionally followed by a parenthesized list of terms, such as Brother (Richard , John). This states, under the intended interpretation given earlier, that Richard the Lionheart is the brother of King John.6 Atomic sentences can have complex terms as arguments. Thus, Married(Father (Richard),Mother (John)) states that Richard the Lionheart's father is married to King John's mother (again, under a suitable interpretation). An atomic sentence is true in a given model if the relation referred to by the predicate symbol holds among the objects referred to by the arguments.

environment taxi

Any taxi driver must deal with a variety of roads, ranging from rural lanes and urban alleys to 12-lane freeways. The roads contain other traffic, pedestrians, stray animals, road works, police cars, puddles, and potholes. The taxi must also interact with potential and actual passengers. There are also some optional choices. PEAS for taxi: Agent Type: Taxi driver Performance Measure: Safe, fast, legal, comfortable trip, maximize profits Environment: Roads, other traffic, pedestrians, customers Actuators: Steering, accelerator, brake, signal, horn, display Sensors: Cameras, sonar, speedometer, GPS, odometer, accelerometer, engine sensors, keyboard

computer vs. brain

Futurists make much of these numbers, pointing to an approaching singularity at which computers reach a superhuman level of performance (Vinge, 1993; Kurzweil, 2005), but the raw comparisons are not especially informative. Even with a computer of virtually unlimited capacity, we still would not know how to achieve the brain's level of intelligence.

agent taxi-driving

In a sense, the performance standard distinguishes part of the incoming percept as a reward (or penalty) that provides direct feedback on the quality of the agent's behavior.

beam search

Population of k individuals, each iteration, generate all neighbors of the current population (or random subset), select the best k neighbors for the next population. The local beam search algorithm3 keeps track of k states rather than just one. It begins with k randomly generated states. At each step, all the successors of all k states are generated. If any one is a goal, the algorithm halts. Otherwise, it selects the k best successors from the complete list and repeats.

BREADTH-FIRST-SEARCH

See pic

Backtracking dynamic

AC-3

The most popular algorithm for arc consistency. To make every variable arc-consistent, the AC-3 algorithm maintains a queue of arcs to consider. (Actually, the order of consideration is not important, so the data structure is really a set, but tradition calls it a queue.)The complexity of AC-3 can be analyzed as follows. Assume a CSP with n variables, each with domain size at most d, and with c binary constraints (arcs). Each arc (Xk,Xi) can be inserted in the queue only d times because Xi has at most d values to delete. Checking consistency of an arc can be done in O(d 2) time, so we get O(cd 3) total worst-case time.

bidirectional search

Two searches: one forward form the root and one backward form the goal. Goal test replaced with frontier intersect test. Requires a method of computing predecessors.

applicable

capable of being applied; relevant, suitable A description of the possible actions ACTIONS available to the agent. Given a particular state s, ACTIONS(s) returns the set of actions that can be executed in s.

acting rationally

doing the best / optimal action. Usually this is based on some sort of objective function. If the objective function(s) is not aligned with human values, it might not behave humanly. Example: studying for an exam.

frame axiom

explicitly FRAME AXIOM asserting all the propositions that remain the same. For example, for each time t we would have Forward t ⇒ (HaveArrowt ⇔ HaveArrowt+1) Forward t ⇒ (WumpusAlivet ⇔ WumpusAlivet+1) · · · where we explicitly mention every proposition that stays unchanged from time t to time t + 1 under the action Forward .

agent program

the actual implementation internally of how the agent maps an percept sequence to an action

behaviorism

the behaviorism movement, led by John Watson (1878-1958), rejected any theory involving mental processes on the grounds that introspection could not provide reliable evidence. Behaviorists insisted on studying only objective measures of the percepts (or stimulus) given to an animal and its resulting actions (or response). Behaviorism discovered a lot about rats and pigeons but had less success at understanding humans.

agent wumpus

the main challenge is its initial ignorance of the configuration of the environment; overcoming this ignorance seems to require logical reasoning. In most instances of the wumpus world, it is possible for the agent to retrieve the gold safely. Occasionally, the agent must choose between going home empty-handed and risking death to find the gold. About 21% of the environments are utterly unfair, because the gold is in a pit or surrounded by pits.

body (of Horn clause)

which is a disjunction of literals of which at most one is positive. So all definite clauses are Horn clauses, as are clauses with no positive literals; these are called goal clauses. Horn clauses are closed under resolution: if you resolve two Horn clauses, you get back a Horn clause.

wumpus world axiom

Propositional logic necessitates a separate axiom for each square and would need a different set of axioms for each geographical layout of the world, first-order logic just needs one axiom: ∀ s Breezy(s) ⇔ ∃r Adjacent (r, s) ∧ Pit(r) Similarly, in first-order logic we can quantify over time, so we need just one successor-state axiom for each predicate, rather than a different copy for each time step.

backward chaining

The establishment of the final link in a behavioral chain, with the addition of preceding links, until the first link is acquired. Inference with Horn clauses can be done through the forward chaining and backward chaining algorithms, which we explain next. Both of these algorithms are natural, in that the inference steps are obvious and easy for humans to follow.

architecture agent

It is natural to ask, "Which of the agent architectures in Chapter 2 should an agent use?" The answer is, "All of them!" We have seen that reflex responses are needed for situations in which time is of the essence, whereas knowledge-based deliberation allows the agent to HYBRID plan ahead.

abstraction

Reducing information and detail to focus on essential characteristics. The simplified representation of a problem

atomic sentence

Same as atom

adversarial search

Searching in the presence of an adversary adds uncertainty, because you don't know what the opponent will do

AND-OR-GRAPH-SEARCH

See pic

AND-SEARCH

See pic

Backtracking chronological

agent utility-based

the "happiness" of the agent. Ie, out of the many possible goal states that the agent could be in, which is the best

action rational

the view that intelligence is concerned mainly with rational action

Backtracking dependency-directed

The basic backjumping method is due to John Gaschnig (1977, 1979). Kondrak and van Beek (1997) showed that this algorithm is essentially subsumed by forward checking. Conflict-directed backjumping was devised by Prosser (1993). The most general and powerful form of intelligent backtracking was actually developed very early on by Stallman and Sussman (1977). Their technique of dependency-directed backtracking led to the development of truth maintenance systems

B* search

argmax

The notation argmaxa∈ S f(a) computes the element a of set S that has the maximum value of f(a).

c (step cost)

The step cost of taking action a in state s to reach state s is denoted by c(s, a, s).

brains cause minds

The truly amazing conclusion is that a collection of simple cells can lead to thought, action, and consciousness or, in the pithy words of John Searle (1992), brains cause minds.

axiom successor-state

The truth value of Ft+1 can be set in one of two ways: either the action at time t causes F to be true at t+1, or F was already true at time t and the action at time t does not cause it to be false. An axiom of this form is called a successor-state axiom and has this schema: F t+1 ⇔ ActionCausesFt ∨ (F t ∧ ¬ActionCausesNotF t) . One of the simplest successor-state axioms is the one for HaveArrow. Because there is no action for reloading, the ActionCausesFt part goes away and we are left with HaveArrowt+1 ⇔ (HaveArrowt ∧ ¬Shoot t) .

backtracking search

a modification to DFS, in which we modify the node in memory and revert the node if a solution is not found.Has O(m) memory, as we need to keep track of the up to m actions performed on the node

agent model-based

a rational agent that uses precept history to create a model of the current state of the world

biconditional

a statement that contains the words "if and only if"

antecedent

a thing or event that existed before or logically precedes another. A sentence such as (W1,3∧P3,1) ⇒ ¬W2,2 is called an implication (or conditional). Its premise or antecedent is (W1,3 ∧P3,1),

AND-OR tree

a tree that represents the complete state-space in a non-deterministic environment

agent learning

after the information gathering, the agent needs to do this to process and improve from what it perceives

agent software agent

agents that exist only in the software world. Like the Amazon recommendation engine

agent autonomous

if the agent can learn and adapt on its own, it has this. Otherwise, the agent behaves completely on prior knowledge and is very fragile

autonomy

if the agent can learn and adapt on its own, it has this. Otherwise, the agent behaves completely on prior knowledge and is very fragile

background knowledge

information that is essential to understanding a situation or problem

agent problem-solving

a goal-based agent that uses an atomic representation of the environment in order to reach the goal.

BESM

A Russian program, BESM may have predated Bernstein's program.

blocks world

A scene from the blocks world. SHRDLU (Winograd, 1972) has just completed the command "Find a block which is taller than the one you are holding and put it in the box." See pic

backing up (in a search tree)

As the recursion unwinds, RBFS replaces the f-value of each node along the path with a backed-up value—the best f-value of its children. In this way, RBFS remembers the f-value of the best leaf in the forgotten subtree and can therefore decide whether it's worth

four-color map problem

Finite-domain constraint satisfaction problems also have a long history. For example, graph coloring (of which map coloring is a special case) is an old problem in mathematics. The four-color conjecture (that every planar graph can be colored with four or fewer colors) was first made by Francis Guthrie, a student of De Morgan, in 1852. It resisted solution— despite several published claims to the contrary—until a proof was devised by Appel and Haken (1977) (see the book Four Colors Suffice (Wilson, 2004)). Purists were disappointed that part of the proof relied on a computer, so Georges Gonthier (2008), using the COQ theorem prover, derived a formal proof that Appel and Haken's proof was correct.

blind search (uninformed)

Go to uniformed

precondition axiom

To avoid generating plans with illegal actions, we must add precondition axioms stating that an action occurrence requires the preconditions to be satisfied.13 For example, we need to say, for each time t, that Shoot t ⇒ HaveArrow t. This ensures that if a plan selects the Shoot action at any time, it must be the case that the agent has an arrow at that time.

effect axiom

To describe how the world changes, we can try writing effect axioms that specify the outcome of an action at the next time step. For example, if the agent is at location [1, 1] facing east at time 0 and goes Forward , the result is that the agent is in square [2, 1] and no longer is in [1, 1]: L(1,1) ∧ FacingEast ∧ Forward ⇒ (L(2,1) ∧ ¬L(1,1)) We would need one such sentence for each possible time step, for each of the 16 squares, and each of the four orientations. We would also need similar sentences for the other actions: Grab, Shoot, Climb, TurnLeft, and TurnRight.

agent function

a function that maps the percept sequence to the an agent's actions

backgammon

a game played with dice and counters on a board divided into two tables each marked with 12 points in which each player tries to move his own counters from point to point and off the board.

adversary argument

an adversary constructing the state space while the agent explores by placing goals and dead ends willy nilly

agent

an agent is something that views its environment through sensors, and acts upon the environment through actuators.

architecture

computing device with physical sensors and actuators

Alldiff constraint

this would be represented as the global constraint Alldiff (F, T,U,W,R,O). The addition constraints on the four columns of the puzzle can be written as the following n-ary constraints: O + O = R + 10 · C10 C10 +W +W = U +10 · C100 C100 + T + T = O + 10 · C1000 C1000 = F , where C10, C100, and C1000 are auxiliary variables representing the digit carried over into the tens, hundreds, or thousands column. These constraints can be represented in a constraint CONSTRAINT hypergraph

breadth-first search

traverse the tree using a FIFO queue. Is complete Is optimal if path cost is non-decreasing TC: O(b^d) SC: O(b^d)

Australia

we are looking at a map of Australia showing each of its states and territories (Figure 6.1(a)). We are given the task of coloring each region either red, green, or blue in such a way that no neighboring regions have the same color. To formulate this as a CSP, we define the variables to be the regions

alpha-beta pruning

when applied to a minimax, helps to prune away game states that will never be playedalpha = highest-value choice we've found so farbeta = lowest-value we've found so far

Analytical Engine

1st general purpose mechanical computer. Ran off of punched cards. Created by Charles Babbage, but never completed by him. The first serious discussion of the feasibility of computer chess and checkers. He did not understand the exponential complexity of search trees, claiming "the combinations involved in the Analytical Engine enormously surpassed any required, even by the game of chess." It included addressable memory, stored programs, and conditional jumps and was the first artifact capable of universal computation.

axon

A threadlike extension of a neuron that carries nerve impulses away from the cell body. Branching out from the cell body are a number of fibers called dendrites and a single long fiber called the axon. The axon stretches out for a long distance, much longer than the scale in this diagram indicates. Typically, an axon is 1 cm long (100 times the diameter of the cell body), but can reach up to 1 meter

heuristic straight-line

Let us see how this works for route-finding problems in Romania; we use the straight line distance heuristic, which we will call hSLD. If the goal is Bucharest, we need to know the straight-line distances to Bucharest, which are shown in Figure 3.22. For example, hSLD(In(Arad))=366. Notice that the values of hSLD cannot be computed from the problem description itself. Moreover, it takes a certain amount of experience to know that hSLD is correlated with actual road distances and is, therefore, a useful heuristic. Figure 3.23 shows the progress of a greedy best-first search using hSLD to find a path from Arad to Bucharest. The first node to be expanded from Arad will be Sibiu because it is closer to Bucharest than either Zerind or Timisoara. The next node to be expanded will be Fagaras because it is closest. Fagaras in turn generates Bucharest, which is the goal. For this particular problem, greedy best-first search using hSLD finds a solution without ever

architecture for speech recognition

The field of speech recognition illustrates the pattern. Many of these were rather ad hoc and fragile, and were demonstrated on only a few specially selected examples. In recent years, approaches based on hidden Markov models (HMMs) have come to dominate the area. Two aspects of HMMs are relevant. First, they are based on a rigorous mathematical theory. This has allowed speech researchers to build on several decades of mathematical results developed in other fields. Second, they are generated by a process of training on a large corpus of real speech data. This ensures that the performance is robust, and in rigorous blind tests the HMMs have been improving their scores steadily. Speech technology and the related field of handwritten character recognition are already making the transition to widespread industrial

Artificial General Intelligence

The field of study that examines relationships between machine intelligence and human intelligence. One branch attempts to shed light on human intelligence by using cyber technology to simulate it.

agent rational

an agent that does the right thing for any particular percept sequence, by maximizing a particular performance measure, it's dependent on what given knowledge the agent has

agent goal-based

builds on top of a model-based agent. This agent has not only a working model but a __, that describes a desired final state. With the model, the agent can choose actions that accomplish this __

attribute

fixed set of variables or attributes, each of which can have a value.

back-propagation

learning algorithms for multilayer networks that were to cause an enormous resurgence in neural-net research in the late 1980s were actually discovered first in 1969 (Bryson and Ho, 1969). A process by which learning can occur in a connectionist network, in which an error signal is transmitted backward through the network. This backward-transmitted error signal provides the information needed to adjust the weights in the network to achieve the correct output signal for a stimulus.

agent logical

operates by deducing what to do from a knowledge base of sentences about the world.

best-first search

when faced with a problem first attempting the solution that most will most quickly solve it Expands nodes base on evaluation function. Use priority queue with estimated cost.

AND node

A node in an AND-OR tree that represents, the possible states an environment can be after an action

A* search

A type of best-first search. search based on a priority queue, where the evaluation function f(n) = g(n) + h(n) where h(n) is the heuristic and h(n) is the path cost from the start node to node n

b∗ (branching factor)

If the total number of nodes generated by A∗ for a particular problem is N and the solution depth is d, then b∗ is the branching factor that a uniform tree of depth d would have to have in order to contain N + 1 nodes. Thus, N + 1 = 1+b∗ + (b∗)^2 + · · · + (b∗)^d.

atomic representation

In an atomic representation ATOMIC each state of the world is indivisible—it has no internal structure. Consider the problem of finding a driving route from one end of a country to the other via some sequence of cities. For the purposes of solving this problem, it may suffice to reduce the state of world to just the name of the city we are in—a single atom of knowledge; a "black box" whose only discernible property is that of being identical to or different from another black box.

bridge card game

The dealer distributes 13 cards to each player, one card at a time, face down, beginning with the player on their left. Each partnership attempts to score points by making its bid, or by defeating the opposing partnership's bid. At the end of play, the side with the most points wins.

automated taxi

agent vacuum

The vacuum agent perceives which square it is in and whether there is dirt in the square. It can choose to move left, move right, suck up the dirt, or do nothing. One very simple agent function is the following: if the current square is dirty, then suck; otherwise, move to the other square. A partial tabulation of this agent function and an agent program that implements it.

branching factor

max(the number of successors for any arbitrary node)In other words, the most children that any node can have. For example, discrete roomba has b=5 (suck, north, south, east west)

And-Elimination

which says that, from a conjunction, any of the conjuncts can be inferred: (α ∧ β, α)/β. For example, from (WumpusAhead ∧ WumpusAlive), WumpusAlive can be inferred.

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