CSCE 420 Final

individuation

division into distinct objects

local consistency

if we treat each variable as a node in a graph and each binary constraint as an arc, then the process of enforcing local consistency in each part of the graph causes inconsistent values to be eliminated throughout the graph.

vanishing point

family of straight lines with directino

cognitive science

field of study that examines how humans and other animals acquire, process, store, and retrieve information, stemmed from the development of computer modeling

three types of queues

fifo, lifo, and priority. fifo is first in first out, lifo is last in first out (aka stack), priority pops off highest priority

toy problem

illustrates or exercises various problem-solving methods.

logical omniscience

if an agent knows a set of axioms, then it nows all consequences of those axioms.

closed-world assumption

if it's not explicitly true, assume false

open-world assumption

if not explicitly true and not explicitly false, it's unknown

static vs dynamic

if the environment can change while the agent is deliberating, the environment is dynamic. Otherwise, static

unknown

if the environment is unknown is has no choice but to take a random action

false negative

if the hypothesis says it should be negative but in fact it is positive.

false positive

if the hypothesis says it should be positive but in fact is negative

cooperative

opposes competitive multi-agent environments, ie avoiding collisions helps the performance measure of all people on the road

Constraint optimization problem

optimally solving CSP's with preferences rather than only requirements.

vlsi layout

position millions of components to minimize area, circuit delay, etc.

quiescent

positions are are unlikely to exhibit wild swings in value in the near future

What are the two choices for representing categories in first-order logic:

predicates and objects

least-constraining value (CSP)

prefers the value that rules out the fewest choices for the neignboring variables in the constraint graph.

objectivist view

probabilities are real aspects of the universe-propensities of objects to behave in certain ways-

n-gram model

probability distribution of n-letter sequences

decision theory =

probability theory + utility theory

relaxed problem

problem with fewer restrictions on the actions

Planning systems

problem-solving algorithms that operate on explicit propositional or relational representations of states and actions

regularization

process of explicitly penalizing complex hypothises

search

process of looking for a sequence of actions

decision tree

represents a function that takes as input a vector of attribute values and returns a 'decision"- a single output value

continuous-valued output attributes

regression tree rather than a classification tree, each leaf is a linear function of some subset of numerical attributes.

behaviorism

rejects any theory involving mental process on the grounds that introspection could not provide reliable evidence

binary constraint

relates two variables in a CSP

factoring

removal of multiple copies of literals

pruned

removed because unwated

belief state

representation of the set of all posible world state it might be in

occlusion

some parts are hidden from some viewing direction

utility

some ways of reaching the goal are better than others. goal is binary, either "unhappy" or "happy". Utility is a spectrum of happiness levels.

singularity

something peculiar or unique, used in the book to refer to futurist's believe in a singularity in which humans perform superhumanly

agent

something that acts

intuition pump

something that amplifies one's prior intuitions

epiphenomenal

something that happens, but casts no shadow, as it were, on the observable world

randomized behavior

sometimes rational in competitive environments because it avoids predictability

weight space

space defined by all possible settings of the weights

holdout cross-validation

splits data, trying to gain most information

backoff model

start by estimating n-gram counts, but for any particular sequence that has a low count, back off to an n-1 gram

Monte Carlo Simulation

start with a search algorithm, from a start position, have the algorithm play thousands of games against itself, using random dice rolls. gives a good approximation of the value of a position.

complete-state formulation

starts with 8 queens and moves them around.

initial state

state the agent starts in

nodes

states in the state space of a problem, usually used in search trees

intentional states

states such as believing, knowing, desiring that refer to some aspect of the external world

probabilistic inference

The computation of posterior probabilities for query propositions given observed evidence

explored set

The set of all states that have been expanded. AKA closed list.

The definition the book chooses to define the goal of AI

act rationally

logicist

hopes to build on logical systems to create intelligent systems

performance measure

"If the sequence is desirable, then the agent has performed well." This is measured by a performance measure

empiricism

"Nothing is in the understanding which was not first in the senses." Locke

physical symbol system hypothesis

"a physical symbol system has the necessary and sufficient means for general intelligent action."

training set of N example input-output pairs

(x1,y1,)(x2,y2),... where each y is generated from unknown function y = f(x), discover approximation function

Turing Test

A computer passes if the human cannot tell whether the written responses come from a person or computer. designed to provide a satisfactory operational definition of intelligence

transition model

A description of what each action does. return the state that results from doing action a in s.

definite clause

A disjunction of literals of which exactly one is positive

probability density function

A function used to compute probabilities for a continuous random variable. The area under the graph of a probability density function over an interval represents probability.

admissible heuristic

A heuristic that never overestimates the cost to reach the goal.

overfitting

A hypothesis is said to be overfit if its prediction performance on the training data is overoptimistic compared to that on unseen data. It presents itself in complicated decision boundaries that depend strongly on individual training examples.

back-propagation

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.

Satisfiability

A sentence is satisfiable if it is true in, or satisfied by, some model.

perceptron

A single, simple, artificial neuron.

repeated state

A state that is exactly the same as a state previously expanded. Lead to by a loopy path.

human-level AI

AI should strive for "machines that think, that learn and the create."

Game playing

AI's can do that.

autonomous planning and scheduling

AI's can do that.

assumption-based truth maintenance system

ATMS, as opposed to JTMS, represents all the states that have ever been considered at the same time.

limited rationality

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

percept

Agent's perceptual inputs at any given instant

fully observable vs partially observable

An agent's sensors give it access to the complete state of the environment at each point in time.

Friendly AI

An artificial intelligence (AI) that has a positive rather than negative effect on humanity.

friendly ai

An artificial intelligence (AI) that has a positive rather than negative effect on humanity.

chronological backtracking

most recent decision point is revisited

DPLL

At each iteration, either eliminate pure literals, unit clauses, or do the split function. For the split function, choose a variable and delete clauses with that variable and delete its negation from clauses. If there is an empty set, return false. If there is an empty clause (you get down to nothing) then it returns true.

Where does reasoning take place?

At the level of categories.

goal formulation

Based on the current situation and the agent's performance measure. The first step of problem solving.

comfirmation theory

Carnap and Hempel, attempted to analyze the acquisition of knowledge from experiences

connectionist models

Cognitive processes depend on patterns of activation in highly interconnected computational networks that resemble neural networks.

expert systems

Computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems

Random-restart hill climbing

Conducts a series of hill-climbing searches from randomly generated initial states, until a goal is found.

global constraint

Constraint involving an arbitrary number of variables

dualist

Descartes considered the mind's activity of thinking and the physical processes of the body to exist in separate realms, what we would now call the dualist theory.

optimality

Does the strategy find the optimal solution?

information gathering

Doing actions in order to modify future percepts

heuristic function

Estimated cost of the cheapest path form the state as node to a goal state.

game theory

Evaluates alternate strategies when outcome depends not only on each individual's strategy but also that of others, does not offer an unambiguous prescription for selecting actions unlike decision theory

best-first search

Expands nodes base on evaluation function. Use priority queue with estimated cost.

adalines

Hebb's learning methods were enhanced by Widrow, and this is what Widrow called his networks.

joint probability distribution

Gives probability of all combinations of values of all variables

Shannon entropy of loaded coin, 99% heads.

H(Loaded) = - (0.99log0.99 + 0.01log.01) = .08 bits

time complexity

How long does it take to find a solution?

space complexity

How much memory is needed to perform the search?

Intelligence

How we think

modus ponens

If P then Q P Therefore Q

subcategory

If basketballs is contained in balls, basketballs is a subcategory of balls

deterministics vs stochastic

If the next state of the environment is completely determined by the current state and the action executed by the agent, the environment is deterministic; otherwise, stochastic. most real-world scenarios should be treated as stochastic.

inheritance

In the object-oriented data model, the ability of an object to inherit the data structure and methods of the classes above it in the class hierarchy. See also class hierarchy.

Completeness

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

In propositional logic, what does the model do?

It fixes the truth value - true or false - for every propositional symbol

model-based, utility-based agent

Looks at sensor and internal state, looks at what will happen if I do this action, judges its utility and uses that to decide on an action.

agent function

Maps any given percept sequence to an action

branching factor

Maximum number of successors of any node

Is there an algorithm that can solve general nonlinear constraints on integer variables?

NO!

constraint graph

Nodes correspond to variables, links connect variables that participate in a constraint

sensorless (conformant) problem

Non-observable Agent may have no idea where it is; solution is a sequence

independence

P(A|B) = P(A) or P(A^B) = P(a)P(b)

bayes rule

P(A|B) = P(B|A)P(A)/P(B)

product rule

P(A ^ B) = P(A|B)(P(B))

causal direction

P(Cause|effect)

Diagnosal direction

P(Effect|Cause)

conditioning

P(Y) = sum of (P(Y|z))P(Z)

inclusion-exclusion principle

P(a v b) = P(a) + P(b) - P(a ^ b)

conditional independence

P(tootache ^ catch|cavity) = P(Tootache|cavity)P(Catch|Cavity)

Four letter acronym composing the task environment

PEAS - performance measure, environment, actuators, sensors

unary constraint

Restricts the value of a single variable

alpha-beta pruning

Returns same move as MiniMax, but prunes branches that won't lead to final decision.

forward pruning

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

knowledge aquisition

Storage of information in long-term memory

Deduction Theorem

The Deduction Theorem states that a sentence φ logically entails a sentence ψ if and only if the sentence (φ ⇒ ψ) is valid.

speech recognition

The process by which computers recognize voice patterns and words, and then convert them to digital data.

simple reflex agent

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

Definitions of Aritificial Intelligence in four categories

Thinking humanly, thinking rationally, acting humanly, acting rationally

True or False: Every sentence of propositional logic is logically equivalent to a conjunction of clauses

True

What are the two propositional symbols that never change?

True and False

minimax decision

action a1 is the optimal choice fro max because it leads to the state with the highest minimax value.

Is commutativity a property of all CSP's?

Yes

Is forward chaining both sound and complete?

Yes

Can a discrete domain be infinite?

Yes, ie the set of integers or string

MAC (Maintaining ARC Consistency)

a CSP algorithm that detects inconsistent arcs.

n-gram model

a Markov chain of order n - 1. Recall that in a Markov chain the probability of character ci depends only on the immediately preceding characters, not on any other characters. In other words, ni_ is a 3-letter n-gram.

ambiguous

a characteristic of all natural languages

naive bayes model

a commonly occuring patterin in which a signel cause directly influences a number of effects, all of which are conditionally independent, given the cause.

k-consistency

a csp is k-consistent if for any set of k-1 variable and for any consistent assignment to those variables, a co nsistent value can always be assigned to the kth

Horn clause

a disjunction of predicates in which at most one of the predicates is positive, not negated

Markov decision process

a formal class of sequential decision problems

fluent

a function or relation that can vary from one situation to the next. ie At(x,l,s).

consistency

a heuristic is consistent if, for every node n and every successor n' of n generated by an 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'.

generalizes

a hypothesis generalizes well if it correctly predicts the value of y for novel examples.

epistemological commitments

a logical agent believes each sentence to be true or false or has no opinion

hill-climbing search algorithm

a loop that continually moves in the direction of increasing value, uphill.

objective function

a mathematical expression that describes the problem's objective

functionalism

a mental state is any intermediate causal condition between input and output

circumpscription

a more powerful and precise version of the closed-world assumption, the idea is to specify particular predicates that are assumed to as "false as possible"- that is, false for every object except for those for which they are known to be true.

leaf node

a node with no children

authority

a page in the set is considered an authority to the degree that other pages in the relevant set point to it.

hub

a page is considered a hub to the degree that it points to other authoritative pages in the relevant set.

foreshortening

a pattern viewed at a slant can be distorted

factored representation

a possible world is represented by a set of variable/variable pairs

sensor model

a probability distribution over the evidence that its sensor provide, given a state of the world

sensor model

a probability distribution over the evidence that its sensors provide, given a state of the world.

full joint probability distributino

a probability model is completely determined by the joint distribution for all of the random variables

contraint satisfaction problem

a problem is solved when each variable of a factored representation has a value that satisfies all the constraints on the variable.

Hebbian learning

a rule invented by Donald Hebb that modified connection strength between neurons

validity (tautology)

a sentence is valid if it is true in all models

Leave-one-out cross-validation

a set of m training instances is repeatedly divided into an m-1 training set and a test set of 1 instance

model

a set of objects and interpretation and an interpretation that maps each name to the appropriate object, relation, or function

explanations

a set of sentences e such that e entails p.

factored representation

a set of variables, each of which has a value

resolution

a single inference rule that yields a complete inference algorithm when couples with any complete search algorithm b

unit clause

a single literal, a disjunction of one literal

constraint propagation

a specific type of inference that uses constraints to reduce the number of legal values for a variable, which can do the same for another variable, and so on.

factored representation of agent program

a state consists of a vector of attribute values

structured representation

a state includes objects, each of which may have attributes of its own as well as relationships to other objects

atomic representation of agent program

a state is a black box with no internal structure

algorithm

a step-by-step procedure for solving a problem

singular extension

a strategy to mitigate the horizon effect, a move that is clearly better than all other moves in a given position. remembered when discovered at any point in the search of a tree.

pure symbol

a symbol that always appears with the same sign in all clauses

rationality

a system is rational if it does the right thing

bootstrap

a technique of loading a program into a computer by means of a few initial instructions which enable the introduction of the rest of the program from an input device.

procedural attachment

a technique whereby a query about a certain relation results in a call to a special procedure designed for that relation rather than a general inference algorithm

CSP arc-consistent

a variable is AC if every value in its domain satisfies the variable's binary constraints

multiagent planning

achieve your goal with help or hindrance of others

serendipity

accidental success

three choices the online agent has on how closely to monitor the environment

action monitoring (before executing, verify all preconditions hold), plan monitoring (before executing an action, verfy that the remaining plan will still succeed), goal monitoring(before executing an action, check to see if there is a better set of goals it oculd be trying to achieve.)

HLA

action that contain other actions, ie "go to san francisco airport"

angelic nondeterminism

agent makes the choices

software agents (software robots/softbots)

agents in software, on the internet usually

Learning agents

agents that are built and then improve because they can learn

machine translation

ai can do that

logistics planning

ai's can do that

robotics

ai's can do that

spam fighting

ai's can do that

pure optimization problems

aim is to find the best state according to an objective function

Manhattan distance

aka city block distance, the sum of the horizontal and vertical distances.

uninformed search algorithm

algorithms that are given no information about the problem other than its definition.

constructive induction algorithms

algorithms that can generate new predicates

logical positivism

all knowledge can be characterized by logical theories connected to observations sentence, combines rationality and empiricism

predecessors of s ate x

all those states that have x as a successor

contingent plans

allow the agent to sense the world during execution to decide what branch of the plan to follow

machine learning

allows computer to adapt to new circumstances and to detect and extrapolate patterns

knowledge representation

allows computer to store what it knows or hears

angelic semantics

allows provably correct high-level plans to be derived without consideration of lower-level implementations

contingent plans

allows the agent to sense the world during execution to decide what branch of the plan to follow

Hierarchical task network (HTN)

allows the agent to take advice from the domain designer in the form of HLA, that can be implemented in various ways by lower-level sequences.

Hierarchical task network

allows the agent to take advice from the domain designer in the form of high-level actions that can be implemented in various ways by lower-level action sequences.

alpha-beta pruning

alpha initialized to negative infinity at a node, beta to positive infinity.

optimal

always finds a global minimum/maximum

loss function

amount of utility lost predicting h(x) = y when the correct answer is f(x) = y.

demonic nondeterminism

an adversary makes the choices

reconstruction

an agent builds a geometric model of the world from an image or a set of images.

recognition approach

an agent draws distinctions among the objects it encounters based on visual nad other information

fundamental idea of decision theory:

an agent is rational iff it chooses the action that yields the highest expected utility, averaged over all the possible outcomes of the action. (MEU, maximum expected utility)

model-based agent

an agent that uses a model

uncertain

an environment is uncertain if it is not fully observable or not deterministic

frames

an idea by Minsky (1975), people followed up with a more structured approach of assembling facts about particular object and event types and arranging the types in a large taxonomic hierarchy, like a biological taxonomy

completeness

an inference algorithm is complete if it can derive any sentence that is entailed.

sound/truth-preserving

an inference algorithm that derives only entailed sentences

utility function

an internalization of the performance measure.

computational linguistics/natural language processing

an intersection of modern linguistics and AI

contrained optimization

an optimization problem is constrained if solutions must satisfy some hard constraints on variable values.

resolvent

an ordinary resolution step takes two clauses c1 and c2 and resolves them to produce the resolvent C. An inverse resolution take C and produces C1 and C2.

computational learning theory

analyzes the sample complexity and computational complexity of inductive learning

axiom

another name for sentence, when a sentence is taken as a given without being derived from other sentences

agent

anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators

unit clause

appears by itself

discrete vs continuous

applies to the state of the environment, way time is handled, and percepts/actions of the agent. If an environment has a finite number of distinct states, it's discrete. Otherwise continuous.

.theorem proving

applying rules of inference directly to the sentences in our knowledge base to construct a proof of the desired sentence without consulting models.

scaled orthographic projection

approximates distances mathematically

expanding the current state

as in search trees, apply each legal action to the current state, generating new states

spatial substances

as opposed to events, mass nouns

automatic assembly sequencing

assembly of things automatically

path cost

assigns a numerical cost to each path

probability model

associates a numerical probability with each possible world

signifigance test

assume there is no underlying pattern. (null hypothesis). find deviation from random. if greater than 5% deviation from random, probably a significant pattern in data.

expressiveness

atomic, factored, and structured representations lie along this axis. a more expressive representation can capture everything a less expressive one can capture, plus more.

Artificial intelligence

attempts not just to understand but to build intelligent entities

recursive best-first search

attempts to mimic the operation of standard best-first search, but uses a linear space.

line search

attempts to solve the hill problem by finding gradients across a line, often doubling the distance with each step, going until the gradient sign changes, to find either local min or local max (the objective goal).

propositional attitudes

attitudes that an agent can have toward mental objects such as believes, knows, wants, intends, and informs. these attitudes do not behave like normal predicates.

Linear interpolation smoothing

backoff model that combines trigram,bigram, and unigram models by linear interpolation

backjumping CSP

backtracks to the most recent assignment in the conflict set

multiple inheritance

banned in some OOP languages because the inheritance algorithm might find two or more conflicting values answering the query.

event calculus

based on points of time rather than on situations

machine evolution (now called genetic algorithms)

based on the belief that by making an appropriate series of small mutations to a machine-code program, one can generate a program with a good performance for any particular task.

Inverse resolution

based on the observation that if the example classifications follow from background ^ hypothesis ^ descriptions, the one must be able to prove this fact by resolution (because resolution is complete).

uniform-cost search

basically bfs with step-cost, instead of expanding shallowest node expand the node with lowest path cost.

local beam search

begins with k randomly generated states. At each step, all successors are generated, if any one is a goal, it halts. otherwise it repeats.

specular reflectino

behavior of a perfect mirro

superpixels

big pixels, over segmentation o of an image containing hunders of homoegenous regions, these homogenous regions are called superpixels

another name for uninformed search

blind search

corpus

body of text

search tree

branches are actions and nodes correspond to states

cognitive science

brings together computer models from AI and experimental psychology to construct testable theories of the human mind

incremental belief-state search

build up the solution one physical state at a time

certainty factors

calculus of uncertainty developed by MYCIN, a program to diagnose blood infections

planning graph

can be constructed incrementally, starting from the initial state. each layer contains a superset of all the literals or actions that could occur at the time step and encodes mutual exclusion relations amont literals or actions that cannot cooccur.

informed search algorithm

can do well given some guidance on where to look for solutions.

depth-limited search

can handle infinite spaces unlike depth-first, because it has a predetermined depth limit l. nodes at depth l are treated like they have no successors. The problem is that it introduces incompleteness of l < d, the shallowest the goal.

State-space search

can operate in the forward direction (progression) or the backward direction (regression)

decision trees

can represent all boolean functions

computable

capable of being computed

inductive logic

capable of computing the correct probability for any proposition from any collection of observations.

extrinsic

categories not maintained under subdivision, ie. length, height of a person

composite objects

categories of these are often characterized by structural relations among parts

natural kind categories

categories with no clear cut definition

subsumption

checking if one category is a subset of another by comparing their definitions

classification

checking whether an objects belongs in a category

stochastic hill climbing

chooses at random from among the uphill moves

backtracking search with csp

chooses values one variable at a time and backtracks when a variable has no legal values left to assign

model preference logic

circumscription is an example, a sentence is entailed if it is true in all preferred models of the KB, as opposed to the requirement of truth in all models in classical logic.

prioritized circumscription

circumscription with priority

environment class

class of environments. ie used to test driverless taxi, many different traffic and weather varieties, pedestrian concentration, etc.

unit clause

clause which has one literal

goal clauses

clauses with no positive literals

tuple

collection of objects arranged in a fixed order and written with angle brackets surrounding the objects

pattern databases

collection of the exact solution costs for every possible subproblem instance- for the 8 puzzle problem every possible configuration of the four tiles and the blank.

A* search

combines g(n), the cost to reach the node from start, with h(n), the cost to get from the node to to the goal. f(n) = g(n) + h(n),

simulated annealing

combines hill climbing with a random walk in some way that yields both efficiency and completeness. isn't greedy in that it sometimes goes down making it possible to be complete, and doesn't go forever in one direction, which is complete but very ineffient.

Decision theory

combines probability theory with utility theory, provides a formal and complete framework for decisions made under uncertainty

total cost

combines search cost and past cost of the solution found

competitive ratio

comparison of the cost with the path cost of the path the agent would follow if it knew the search space in advance.

loss function

tells us how bad each error is

four ways to measure an algorithm's performance

completeness, optimality, time complexity, space complexity

offline search algorithm

compute a complete solution before setting foot in the real world and then execute the solution.

minimax algorithm

computes the minimax decision from the current state

architecture

computing device with physical sensors and actuators

agent program

concrete implementation running in a physical system, unlike an agent function

conjunctive normal form

conjunction of clauses

beam search

consider only a beam of the n best moves rather than considering all possible moves. this is dangerous because there is no guarantee that the best move will not be pruned away.

utility-based agent

considers not goals (whether or not I've reach the goal determines whether I'm happy or sad), but a spectrum of happiness levels, an internalized performance measure.

Narrow content

considers only the brain state

atomic sentences

consist of a single proposition symbol

constraint hypergraph

consists of ordinary nodes (circles) and hypernodes (squares) which represent n-ary constraints.

evaluation function

construed as a cost estimate, the node with the lowest evaluation is expanded first.

observation sentences

correspond to sensory inputs

disjoint pattern databases

create a lower bound on subproblem complexity by finding the number of possible moves in the subproblem.

model selection

defines the hypothesis space

logical minimization

defining an objects as the smallest one satisfying certain conditions

unconditional or prior probabilities

degrees of belief in propositions in the absence of any other information

internal state

depends on the percept history and thereby reflects at lesat some of the unobserved aspects of the current state

subjectivist/Bayesian

describes proabiltiies as a way of characterizing an agent's beliefs, rather than as having an external physical significance.

goal info

describes situations that are desirable

object model

describes the objects that inhabit the visual world

object modle

describes the objects that inhabit the visual world-people, building, trees, cars, etc.

rendering model

describes the physical, gemoetric, and statistical process that produce the stimulus from the wrodl

rendering model

describes the physical, geometric, and statistical process that produce the stimulus from the world.

truth maintenance system

designed to handle belief revisio

background subtraction

detect background and subtract it

goal test

determines whether a given state is a goal state

homeostatic devices

devices that contain appropriate feedback loops to achieve stable appropriate behavior

nonmonotonic logics

devised with modified notions of truth and entailment in order to capture this behavior

graph

directed network

feature selection

discard attributes that appear to be irrelevant

partition

disjoint exhaustive decompsition

clause

disjunction of literals(thing ored together)

straight-line distance heuristic

draw straight line from x to y, use to inform some search algorithm

justification-based truth maintenance system

each sentence in the knowledge base is annotated with a justification consisting of the set of sentences from which it was inferred

triangle inequality

each side of a triangle cannot be longer than the sum of the two sides

linear constraints

each variable appears in linear form in a constraint equation in CSP

global maximum

elevation corresponds to an objective function in a state-space function, find the global maximum

state-space landscape

elevation corresponds to either cost or the objective function

feature extraction

emphasizes simplme computations applied directly to sensor observations

probability theory

enable a degree of belief

Natural language processing

enables successful communication in english

semidynamic

environment itself doesn't change but performance measure does, it's semidynamic

randomize

escape from infinite loops is possible if the agent can randomize his actions

stochastic games

essentially, a mixture of luck and skill. there is a random element

local search

evaluating and modifying one or more current states rather than systematically exploring paths from an initial state. operate using a single current node and move on to neighbors of that node.

practical ignorance

even if we know everything, we might be uncertain about a particular patient because not every test has been runo

discrete events

events with a definite structure

traveling salesperson problem

everything must be visited exactly once

bunch

example: BunchOf({Apple1,Apple2,Apple3})

redundant paths

exists whenever there is more than one way from one state to another

depth-first search

expands the deepest node in the current frontier of the search tree.

Greedy Best First Search

expands the node that is closest to the goal. on the grounds that this is likely to lead to a solution quickly.

assumptions

explanations can include assumptions-sentences taht are not known to be true, but would suffice to prove p if they were true.

temporal logic

facts hold at particular times and that those times are ordered

constraint learning (CSP)

find a minimum set of variables from the conflict set that causes the problem

route-finding problem

find optimal route

continuous and integer-valued input attributes

find split point with highest information gain

iterative deepening search

finds the best depth limit, general strategy often used with depth first search. combines the benefits of BFS and DFS by iteratively increasingly the depth that it uses for the depth limit. 4. in general, iterative deepening is the preferred uninformed search method when the search space is large and the depth of the solution is not known.

optimization

finds the best hypothesis within a spect

complete local search algorithm

finds the goal if it exists

Existential Instantiation

for any sentece alpha, variable v, and constant k that does not appear elsewhere in the knoweldge base, we can use the existence thing and take out the variable

principle of tichromacy

for any spectral energy density, no matter how complicated, it is possible to construct another spectral energy density consisting of a mixture of just three color-red, gree, nd blue- such that a stupid ****in human can't tell the difference.

definition of a 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.

default logic

formalism in which defautl rules can be written to generate continuous, nonmonotonic conclusions

Three steps of search

formulate, search, execute

data-driven reasoning

forward chaining is an example of this- reasoning in which the focus of attention starts with the known data

And-elimination

from a conjunction, any of the conjuncts can be inferred (if a and b then a)

lens system

gather sufficient light while keeping the image in focus

upper ontology

general framework of concepts

countours

general outline of cost driven over a search tree, kind of like on a topological map. possible with a star

induction

general rules are acquired by exposure to repeated associations between their elements

weak methods

general-purpose search mechanisms trying to string together elementary reasoning steps to find complete solutions

environment generator

generate environments with certain parameters, likelihood of certain events, etc.

dropping conditions

generates a weaker definition and allows a larger set of positive examples

applicable

given a state s, there is a set of actions. These actions are applicable in s.

text classification/categorization

given a text of some kind, decide which of a predefined set of classes it belongs to.

language identification

given a text, determine what natural language it is written in

protein design

goal is to find a sequence of amino acids that will fold into a 3-d design with the properties to cure a disease

8-queen problems

goal is to place eight queens on a chessboard such that no queen attacks an other.

existential grpahs

graphical notation of nodes along edges

hill climbing is often called a

greedy local search algorithm

population/individual

group of things put there in genetic algorithm. group of tries.

truth maintenance systems

handle knowledge updates and revisions efficiently

conditional/posterior probability

have givens.

early stopping

having an algorithm stop generating nodes when there is no good attribute to split on, rather than going to all the trouble of generating nodes and then pruning them away

semantic

having to do with the meaning of words or language

another named for informed search

heuristic search

Lisp

high-level programming language, the dominant AI programming language

fitness function

higher values for better states

queue

how a frontier needs to be expanded in a way that the search algorithm can easily choose the next node to expand according to its preferred strategy. operations are empty, pop, and insert

learning

how all agents can improve performance

exploration

how an agent discovers an unknown environment

inheritance

how categories serve to organize and simplify the knowledge base

knowledge representation language

how each sentence is expressed, represent some assertion about the world

syntax

how sentences are expressed

taxonomy

how subclass relations organize categories

actuators

how the agent acts on its environment

sensors

how the agent perceives its environment

logical inference

how the definition of entailment can be used to derive conclusions

accessibility relatinos

how the worlds are connected in modal logic, one relation for each modal operator. we say that w1 is accessible from world w0 with respect to ka if everything in w1 is consistent with what A knows in w0.

learning curve/happy graphs

how we can evaluate the accuracy of a learning algorithm, represented by graph in which each point represents a specific number of trials, and on the y axis you have the proportion correeect, as set grows, happiness should increase

decision tree pruning

how we combat overfitting. eliminate nodes that are not relevant

test set

how we measure the accuracy of a hypothesis

consistent hypothesis

hypothesis that agrees with all the data

m is a model of alpha

if a sentence alpha is true in model m

satisfies

if a sentence alpha is true in model m, we say that m satisfies alpha.

ground resolution theorem

if a set of clauses is unsatisfiable, then the resolution closure of those clauses contains the empty clause

first-choice hill climbing

implements stochastic hill climbing by generating successors randomly until one is generated that is better than the current state

agent program

implements the agent function

omniscience

impossible in reality, an omniscient agent knows the actual outcome of its actions and can act accordingly

partial assignment

in CSP solving, it's when some variables are not assigned.

constraint weighting

in CSP's, give weight to constraints. adds more weight to the difficult to solve constraints

precedence constraints

in CSP's, something must happen before something else.

complete assignment

in a constraint satisfaction problem, it's when every variable is assigned a value. A CSP is a consistent, complete assignment.

knowledge-based agents

in ai, an approach to intelligence that states that people learn through processes of reasoning that operate on internal representations of knowledge.

episodic vs sequential

in an episodic task environment, experience is divided into atomic episodes of of the agent receives a percept and performs a single action.

incompleteness theorem

in any formal theory, there are true statements that are undecidable in the sense that they have no proof within the theory.

node-consistent

in csp, a variable is node-consistent if all the values in the domain satisfy its unary constraints.

MRV heuristic

in csp, picks a "most constrained variable" or a "fail first" variable.

skolem constant

in instantiation, what you call something when you don't want it to be mistaken as a variable or something

known vs unkown

in known, outcomes for all actions are given.

competitive

in maximizing your performance measure you seek to minimize that of others

possible worlds

in modal logic, need possible words rather than just one world of truth

alliances

in multiplayer games, these are agreements

reference class problem

in trying to determine the outcome probability of a particular experiment, the frequentist has to place it in a reference class of "similar experiments with known outcome frequencies.

total Turing Test

includes video signal

evidence

information

entropy

information gain , the fundamental quantity in information theory

critic (as related to the learning agent)

informs the learning element on how the agent is doing and determines how the performance element should be modified to do better in the future.

Problem's five components

initial state, possible actions available, description of what each action does, goal test (whether an agent is or isn't in the goal state), path cost(numeric cost to each path)

online search

interleaves computation and action. first it takes an action, then it observes the environment and computes the next action. Good in an environment where there is a penalty for sitting around and computing for too long.

deformation

internal degrees of freedom of othe object changes its appearance.

wide content view

interprets it from the point of view of an omniscient outside observer with access to the whole situation, who can distinguish differences in the world

Bayesian network

invented to allow efficient representation of uncertain knowledge

incremental formulation

involves operators that augment the state description, starting with an empty state.

referential transparency

it doesn't matter what term a logic uses to refer to an object, what matters is the object that the term names.

strongly k-consistent

k-consistent, (k-1) consistent, (k-2) consistent, and so on..

tabu search

keeping a small list of recently visited states and forbidding the algorithm to return to those states

model

knowledge about how the world works

logic

laws of thought supposed to govern the operation of the mind

trying to use logic to cope with a domain like medical diagnosis fails for three main reasons:

laziness, theoretical ignorance, practical ignorance

solution

leads from an action state to a goal state

touring problems

like route finding, but they involve going to a certain number of things, so where you have traversed must be recorded in internal state

robot navigation

like route-finding but continuous

probability

likelihood that a particular event will occur

microworlds

limited problems that appear to require intelligence to solve

mass nouns

linguistic synonym for stuff

count nouns

linguistic synonym for things

probability distribution

list of possible outcomes with associated probabilities

pure literal

literal which appeals with the same truth value throughout a sentence

what features often trap hill-climbing

local maxima, ridges, plateaux

gradient

local slope, often used to find a maximum.

canonical representation

logically equivalent states should map to the same data structure

optimal solution

lowest path cost among all solutions

global minimum

lowest valley in a state-space landscape. if elevation corresponds to cost, this is the goal.

weak AI

machines could act as if they were intelligent

strong AI

machines that do so are actually thinking

current-best-hypothesis

maintain a single hypothesis, and to adjut it as nex examples arrive in order to maintian consistency.

satisficing

making decisions that are "good enough", rather than laboriously calculating an optimal decision

exhaustive decomposition

males and females constitute an exhaustive decomposition of the animals if, if you are not a male you are a female animal.

policy

mapping from every possible state to the best move in that state

model

mathematical abstractions which simply fix the truth or falsehood of every relevant sentence.

utility

mathematical treatment of "preferred outcomes"

diameter of state space

max number at which any node can be reached by any other node

noisy values

may return different values for f(x) each time x occurs

semantics

meaning of sentences

theroetical ifnorance

medical science has no complete theory for a domain

MA*

memory-bounded a star

biological naturalism

mental states are high-level emergent features that are caused by low-level physical processes in the neurons, and it is the properties of the neurons that matter.

large-scale learning

millions of examplees

minimum description length

minimizes the total number of bits required.

hidden Markov Models

models for speech recognition based on a rigorous mathematical theory and generated by a process of training on a large corpus of real speech data

total function

models in first-order logic require total functions, that is, there must be value for every input tuple

language models

models that predict the probability distribution of language expression

chance nodes

must be incorporated along with min and max nodes in stochastic games

multiagent planning

necessary when there are other agents in the environment with which to cooperate or compete

robotics

needed for total turing test,allows to manipulate objects and move about

computer vision

needed for total turing test. allows to perceive objects

constraint language

needed to solve CSP's with infinite domains.

neurons

nerve cells

dead-end

no goal state is achievable

optimally efficient

no other optimal algorithm is guaranteed to expand fewer nodes, A star is an example.

child nodes

nodes that are children of parent nodes (one level below)

process categories/liquid event categories/temporal substances

not discrete events, subcategories of discrete events

tractability

not intractable, ie time required to solive does not grow exponentially with the size of the instances

modal logic

not just concerned with the modality of truth. involves sentences as arguments, rather than terms.

with competitive partially observable games, what is the goal?

not just to move pieces to the right squares but also to minimize the information that the opponent has about their location.

description logics

notations that are designed to make it easier to describe definitions and properties of categories

sample complexity

number of required examples of the hypothesis space

depth

number of steps from root

frequentist

numbers can come only from experiments

probabilistic agent

numerical edegree of belief between 0 and 1

aspect

object look different when seen from different directions

motion blur

objects in the scene that move will apear blurred because they send photons.

communication

often emerges as a rational behavior in multiagent environments

minimum slack algorithm

on each iteration, schedule fo rht earliest possible start whichever unscheduled actions has all its predecessors scheduled and has the least slack

execution

once a solution is found, the actions it recommends can be carried out.

nondeterministic environment

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

complementary literals

one literal is the negation of another

ambiguity

one name for two or mroe different categories

sum of squared differences

one possible measure of similarity

rational agent

one that acts so as to achieve the best outcome, or , given uncertainty, the best expected outcome.

Syllogisms

patterns for argument structures that always yield correct conclusions when given correct premises

real-world problem

people care about it.

what are six risks given by the book that can be caused by AI

people might lose their jobs to automation people might have too much leisure time people might lose their sense of uniqueness AI systems might be used toward undesirable ends the use of AI systems might result in a lack of accountability the success of AI might mean the end of the human race

neats

people who think that AI theories should be grounded in mathematical rigor

diffuse albedo

percent of light you reflect

reward or penalty (learning agent)

performance standard distinguishes part of the incoming percept as reward or penalty, provides direct feedback on the quality of the agent's behavior.

topological sort

pick any variable to be the root of the tree, and choose an ordering of the varibles such that each variable appears after its parent in the tree.

pinhole camera

pinhole opening, o, and an image plane at the back of the box.

specularites

places of perfect or near perfect reflection

wrappers

programs taht extract information from a page

error rate of hypothesis

proportion of mistakes it makes

principle of indifference

propositions that are syntacticalkly "symmetric" with respect to the evidence should be accorded equal probability

description logic

provide a formal language for constructing and combing category definitions and efficient algorithms for deciding subset and superset relationships between categories.

semantic networks

provide graphical aids for visualizing a knowledge base and efficient algorithms for inferring properties of an object on the basis of its category membership

NP-completeness

provides a method to recognize an intractable problem.

closed-world assumption

provides a simple way to avoid having to specify lots of negative information

control theory

purposive behavior arises from a regulatory mechanism trying to minimize error- the difference between a current a goal state.

predicate indexing

putting the things in the right places, eg putting all the Knows in one bucket and Brothers in the other, just so as not to confuse anything.

mutation

random change

depth of filed

range in which you can focus

stochastic beam search

rather than choosing the best k from the pool of candidate successors, it just randomly generates k. Solves the diversity problem

intelligence is mainly concerned with __________

rational action

performance element

responsible for selecting external actions

problem generator (as relates to learning agent)

responsible for suggesting actions that will lead to new and informative experiences. suggests exploratory actions that might be suboptimal in the short run.

learning element

responsible fro making improvements in a learning agent

PAC learning algorithm

returns approximately correct hypothesis

retrograde minimax search

reverse the rules of chess to do unmoves rather than moves. any move by white that, no matter what move black responds with, ends up in a position marked as a win, must also be a win.

inference rules

rules that can be applied to derive a proof

diffuse reflection

scatters light evenly across the direction leaving a surface so the brightness of a diffuse surface doesn't depend on the viewing direction

active sensing

send out a signal, and sense the reflection of this signal off of the environment

path

sequence of state connected by sequence of actions.

decision lists

series of tests, each of which is a conjunction of literals

frontier (aka open list)

set of all leaf nodes available for expansion

hypothesis space

set of all possible hypotheses

sample space

set of all possible words

version space

set of hypothesis remaining

grammar

set of rules of a language

Knowledge base

set of sentences, the central component of a knowledge-based agent

language

set of strings

conflict set (CSP)

set that are in conflict with a variable

Kolmogorov's axioms

showed how to build up the reset of probabiltiy theory from this simple foundation and how to handle the difficulties caused by continuous variables.

simple distinction between four agents.

simple reflex agents repond directly to precepts. model-based reflex agents maintain internal state that aren't evident in current percept. goal-based agents act to achieve goals. utility-based agent try to maximize expected "happiness"

discrete, finite domains

simples CSP's, ie map coloring and scheduling with time limits, eight-queen problem

ockham's razor

simplest hypothesis is preferable

sma*

simplified ma star

underconstrained

solutions are densely distributed

preference constraint (CSP)

solutions are preferred, not necessarily required

safely explorable

some goal state is reachable from every reachable state

instances

strings that match a schema

ontological engineering

study of how to create representations, concentrating on general concepts-such as events,times,physical objects, and beliefs-that occur in many different domains.

operations research

study of how to take rational decisions when payoffs from actions are not immediate but instead result from several actions taken in sequence.

qualitative physics

subfield of AI that investigates how to reason about physical systems without plunging into detailed equations and numerical simulations

schema

substring in which some of the positions can be left unspecified

margnization/summing out

summation of the probabilitiesi for each possible value of the other variables, thereby takeng them out of the equation.

Model-based agent sequence

takes in sensor data, looks at state, analyzes how the world has evolved and what past actions have done, tries to find out more info about unobserved aspects of the state. Makes decision in same way as model-based agent.

intelligent agent

takes the best possible action in a situation

linear regression

task of finding the hw that best fits the data

discretize

technique in local search, solve continuous problems by discretizing neighborhood of each state.

deformable template

tells us which configurations are acceptable: the elbow can bend but the head is never joined to the foot.

ground term

term without variables

PDLL

the Plnning Domain Definition Langugae, describes the initial nad goal states as conjunctions of literals, and actions in terms of the preconditions and effects

game

the actions of one player can significantly affect the utility of another

transhumanism

the active social movement that looks forward to this future in which humans are merged with -or replaced by-robotic and biotech inventions.

unobservable

the agent has no sensors at all, the environment is unobservable

reinforcement learning

the agent learns from a series of reinforcements-rewards or punishments

unsupervised learning

the agent learns patterns in the input even though no explicit feedback is supplied

supervised learning

the agent observes some example input-output pairs and learns a function that maps from input to output.

supervised learning

the available feedback provides the correct answer for example inputs

materialism

the brain's operation according to the laws of physics constitutes the mind

effective branching factor

the branching factor that a uniform tree of depth d would have to have in order to contain n+1 nodes

percept sequence

the complete history of everything the agent has ever perceived

in an inference, what is the right side called (the side pointed to)?

the conclusion or consequent

grounding

the connection between logical reasoning processes and the real environment in which the agent exists.

specializtion

the extension of the hypoteheis must be decreased to exclude the exmaple

realizable

the hypothesis space contains the true function

entailment

the idea that a sentence follows logically from another sentence

belief state

the key concept required for solving partially observable problems. it represents the agent's current blief about the possible physical state it might be in, given the sequence of actions and percepts up to that point.

perceptron convergence theorem

the learning algorithm can adjust the connection strengths of a perceptron to match any input data, provided such a match exists

argument from consciousness

the machine has to be aware of its own mental states and actions (from Can Machines Really think?)

monism/physicalism

the mind is not separate from the body-mental states are physical states

goal predicate

the name of the output for a decision tree

small-scale learning

the number of training examples ranges from dozens to low thousands

critical path

the path whose total duration is longest

What is rational depends on: (four things)

the performance measure (defines the criterion of success), the agent's prior knowledge of the environment, the actions that the agent can perform, the agent's percept sequence to date.

epistemological commitment

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

rationalism

the power of reasoning in understanding the world

In an inference, what is the left side called (the side not pointed to)?

the premise or antecedent

Markov chain

the probability of charater ci depends only on the immediately preceding characters, not on any other characters

task environment

the problems to which rational agents are the solutions

information extraction

the process of acquiring knowledge by skimming a text and looking for occurences of a particular class of object and for relatinoships among objects

smoothing

the process of adjusting the frequency of low-frequency counts

segmentation

the process of breaking an image into regions of similar pixels

problem formulation

the process of deciding what actions and state to consider, given a goal

abstraction

the process of removing detail from a representation

horizon effect

the program is facing an opponent's move that causes serious damage and is ultimately unavoidable, but can be temporarily avoided by delaying tactics.

recall

the proportion of all relevant documents in the collection that are in the result set.

Precision

the proportion of documents in the result set that are actually relevant

perplexity

the reciprocal or probability , normalized by sequence length.

state space

the set of all states reachable from the initial state by any sequence of actions.

monotonicity

the set of entailed sentences can only increase as information is added to the knowledge base

boundary set

the set of hypothesis composing the boundary

vocabulary

the set of symbols that make up the corups and the model

iterative-deepening A*

the simplest way to reduce memory requirement for a start is to adapt the idea of iterative deepening to the heuristic search context.

phenomenology

the study of direct experience

neuroscience

the study of the nervous system, particularly the brain.

inverted spectrum thought experiment

the subjective experience of person X when seeing red objects is the same experience that the rest of us experience when seeing green objects, and vice versa.

decentralized planning

the subplan constructed for each body may need to include explicit communicative actions with other bodies

information retrieval

the task of finding documents that are relevant to a user's need for information.

qualification problem in AI

the the inability to capture everything in a set of logical rules

minimax value

the utility of being in the corresponding state, assuming that both players play optimally from there to then end of the game.

expected utility

the utility the agent expects to derive, on average, given the probabilities and utilities of each outcome.

weights

the value of y is changed by changing the relative weight of one term or another

marginal probability

the values in the margins of a joint probability table that provide the probabilities of each event separately

ontological commitments

the world is composed of facts that do or do not hold in any particular case

single vs multiagent

there are multiple agents involved which have a goal of maximizing a performance measure dependent upon other agents

dualism

there is a part of the human mind outside of nature

stationary distribution

there is a probability distribution over examples that remains stationary over time.

intrinsic

things that belong to the very substance of an object

stuff

things that cannot be individuated ie, butter, water, etc.

scruffies

those who would rather try out lots of ideas, write some programs, and assess what seems to be working

path consistency (CsP)

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

autonomy

to the extent that an agent relies on the prior knowledge of its designer rather than on its own percepts, we say an agent lacks autonomy

laziness

too much work to list complete set of antecedents, etc.

True or false: if kb is true in the real world, then any sentence alpha derived from kb by a sound inference is also true in the real world

true

reification

turning a proposition into an object, ie (Basketball(b)) into basketballs

synonymy

two names for the same category

disjoint

two or more categories are disjoint if they have nothing in common

logical equivalence

two sentences a and b are logically equivalent if they are true in the same set of models.

disjunctive constraint

two things must not overlap in time (CSP)

problem-solving agent

type of goal-based agent that uses atomic representation.

planning agent

type of goal-based agent that uses more advanced factored or structure representations

breadth first search

type of uninformed search, a simple strategy in which the root node is expanded first, then all of the successors are expanded, then their successors, etc.

bidirectional search

type of uninformed 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.

clusternig

type of unsupervised learning: detecting potentially useful clusters of input examples

search cost

typically depends on time complexity but also can include a term for memory usage

missing data (dt)

unrecoded values or data that might be too expensive to obtain

hypothesis

when a learning agent derives an approximation function

automated reasoning

use stored info to answer questions and to draw new conclusions

backed-up value

used by recursive best-first search. as recursion unwinds rbfs replaces f value of each nodel with teh best f value of its children, the backed up value.

condition-acting rule

used by simple reflex agent. If this happens in environment do this.

memoization

used in computer science to speed up progams by saving the results of computation

sensorless/conformant planning

used to construct a plan that works without the need for perception.

goal-directed reasoning

useful for answering questions such as "what shall I do now?" Backward chaining is an example

online planning agent

uses execution monitoring and splices in repairs as needed to recover from unexpected situations,

online planning agent

uses execution monitoring and splices in repairs as needed to recover from unexpected situations.

goal-based agent

uses goal information

goal-based, model-based agent

uses internal state and current sensor readings to look at what will happen if I do x, then looks at the series of goals to define the action.

dominated

when a task is the same as another task B, but B is cheaper and faster.

backtracking search

variant of depth-first search, uses less memory. only one successor is generated at a time, each partially expanded node remembers which successor to generate next.

genetic algorithm

variant of stochastic beam search in which successor states are generated by combining two parents states rather than by modifying a single state.

robotic vehicles

vehicles that are robotic

cognitive psychology

views the brain as an information-processing device

higher-order logic

views the relations and functions referred to by first-order logic as objects in themselves

semi-supervised learning

we are given a few labeled examples and must make what we can of a large collection of unlabeled examples

Universal Instantiation

we can infer any sentence obtained by substituting a ground term for the variable

knowledge level

we need specify only what the agent knows and what its goals are, in order to fix its behavior

ontological commitment

what a language assumes about the nature of reality

image

what is created by image sensors

environment

what is perceived by the agent

solution

what is returned by a search algorithm with an input, takes form of action sequences when relates to search algorithm

background knowledge

what the KB initially contains

random variables

what variables are called in probability theory, start with capital letters

uncertainty

when an agement doesn't know for certain what state it's in or where it will end up after a sequence of actions

multivalued attributes

when an attribute has many possible values, the information gain measured gives an inappropriate indication of the attribute's usefulness.

optical flow

when an object in a video is moving or the came is moving relative to an object

conditional effect

when condition:effect

belief revision

when inferred facts turn out to be wrong and will have to be retracted in the face of a new information

regression

when the output y is a number, the learning problem is called a regression

classification

when the output y is one of a finite set of values, the learning problem is called classification

nonmonotonicity

when the set of beliefs does not grow monotonically over time as new evidence arrives

loopy path

when you loop in path to a solution. usually bad.

forward-checking CSP

whenever a variable is assigned, the forward-checking process establishes arc consistency for it, ie 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.

scene

where image sensors gather light

intentionality

whether the machine's purported beliefs, desires, and other representations are actually "about" something in the real world

consistency

whether the membership criteria are logically satisfiable

open-loop

while the agent is executing the solution sequence it ignores its percepts because it knows what they will be. because it executes with its eyes closed, it must be certain of what is going on. this is called open-loop because ignoring the percepts breaks the loop between agent and environment

localization

working out where you are given a map of the world and a sequence of percepts and actions

unit resolution

you have a list of things or'd together and'd with a complementary literal of something in the list, then now you have a list without that literal