CSC 516 Exam 1 vocab set 1
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.
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
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.
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.
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.
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.
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.
Backtracking chronological
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.
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 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
B* search
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."
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."
agent function
a function that maps the percept sequence to the an agent's actions
admissible heuristic
a heuristic which doesn't overestimate the actual path cost Allows the A* algorithm to be optimal
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 __
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 .
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.
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.
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.
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.
Backtracking dynamic
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.
c (step cost)
The step cost of taking action a in state s to reach state s is denoted by c(s, a, s).
action rational
the view that intelligence is concerned mainly with rational action
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.
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.
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
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.
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
See pic
AND-OR-GRAPH-SEARCH
See pic
AND-SEARCH
See pic
BACKTRACK
See pic
BACKTRACKING-SEARCH
See pic
BREADTH-FIRST-SEARCH
See pic
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).
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)
agent logical
operates by deducing what to do from a knowledge base of sentences about the world.
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
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 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
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
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.)
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
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
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.
argmax
The notation argmaxa∈ S f(a) computes the element a of set S that has the maximum value of f(a).
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.
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) .
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.
agent reflex
These agents select actions on the basis AGENT of the current percept, ignoring the rest of the percept history
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
axiom
a universally recognized principle; premise; postulate; self-evident truth
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.
blind search (uninformed)
Go to uniformed
branching factor effective
b∗ (branching factor)
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.
BESM
A Russian program, BESM may have predated Bernstein's program.
AND node
A node in an AND-OR tree that represents, the possible states an environment can be after an action
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
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.
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
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
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
actuator
a mechanism that puts something into automatic action
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.
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.
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.
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.
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.
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.
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.
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
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.
architecture
computing device with physical sensors and actuators
attribute
fixed set of variables or attributes, each of which can have a value.
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
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.
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.
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.