Glossary for Artificial Intelligence: A Modern Approach

A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z

8-puzzle

8-puzzle consists of a 3x3 grid containing 8 numbered tiles and a blank space. A tile adjacent to the blank space can slide into that space. The object is to reach a specified goal state from a given initial state.

A

absolute error

Magnitude of the difference between the theoretical value (expected value) and the actual value of a physical quantity.

abstraction

Abstraction is selecting data from a larger pool to show only the relevant details to the object.

abstraction hierarchy

It hides the complexity of the system and allows individuals to work on different modules of the hierarchy at the same time.

accessibility relations

In modal logic, an accessibility relation R is a binary relation such that R⊆ W×W where W is a set of possible worlds. The accessibility relation determines for each world w ∈ W which worlds ẃ are accessible from w.

action monitoring

Checking the preconditions of each action as it is executed, rather than checking the preconditions of the entire remaining plan.

action schema

action-utility function

actions

The things that an agent can do. We model this with a function, Actions(s), that returns a collection of actions that the agent can execute in state s.

activation

activation function

A mathematical function that transforms the input or set of inputs received at a neuron to produce an output. Popular examples include the Sigmoid function, Rectificied Linear Units (ReLU) and Hyperbolic Tangent (Tanh)

active learning

An active learning agent decided which actions to take in order to guide its learning: it values leearning new things as well as reaping immediate rewards from the environment. This is in contrast to a passive learning agent, which learns from its observations, but the actions the agent takes are not influenced by the learning process.

actor

adaptive dynamic programming

Also known as Approximate Dynamic Programming; it is a type of Reinforcement Learning where local rewards and transitions depend on unknown parameters - we set an initial control policy and update it until it converges to an optimal control policy.

add list

admissible heuristic

A heuristic is a function that scores alternatives at each branching in a search algorithm. An admissible heuristic is one that never overestimates the cost to reach the goal. Admissible heuristics are optimistic in nature as they believe the cost of reaching the goal is less than it actually is.

adversarial search

Traversing a tree data structure to find all possible moves. It is usually used in a two-player game; each available move is represented using gain and loss for an individual player. An important application of it is in zero sum games, as in those games, one players' loss is the other players' gain.

adversary argument

agent

An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators.

agent function

An agent's behavior is described by the agent function that maps any given percept sequence to an action.

agent program

Internally, the agent function for an artificial agent will be implemented by an agent program.

agglomerative clustering

It is a category of hierarchical clustering which uses a bottom-up approach. All observations start in their own cluster and different pairs of clusters are merged as you move up levels in the hierarchy. Its results are represented using a dendrogram.

aggregation

algorithm

An algorithm is a sequence of unambiguous finite steps that when carried out on a given problem produce the expected outcome and terminate in finite time.

alignment method

alpha-beta

alpha (α) is the value of the best (i.e., highest-value) choice we have found so far at any choice point along the path for MAX and beta(β) is the value of the best (i.e., lowest-value) choice we have found so far at any choice point along the path for MIN in a standard minimax tree.

alpha-beta pruning

alpha—beta pruning is applied to a standard minimax tree to prune away branches that cannot possibly influence the final minimax decision.

ambient illumination

Light that is already present in a scene, before any additional lighting is added. It usually refers to natural light.

ambiguity

It is the state of being uncertain or doubtful.

ambiguity aversion

Ambiguity aversion (also known as uncertainty aversion) is a preference for known risks over unknown risks.

analogical reasoning

Analogical reasoning is any type of thinking that relies upon an analogy. An analogical argument is an explicit representation of a form of analogical reasoning that cites accepted similarities between two systems to support the conclusion that some further similarity exists.

anchoring effect

It is a type of cognitive bias which makes people focus on the first piece of information (the "anchor") that was given to them, to make decisions. To explain this with an example; when buying a product if you're told a high price by the seller, your mind estimates the worth of that product as that initial/anchor price you're told, and then when you're offered a discount on it, you are more inclined to buy it thinking that you're getting it for cheap.

And-Elimination

In propositional logic, conjunction elimination (also called and elimination, ∧ elimination, or simplification0 is a valid immediate inference, argument form and rule of inference which makes the inference that, if the conjunction A and B is true, then A is true, and B is true. The rule makes it possible to shorten longer proofs by deriving one of the conjuncts of a conjunction on a line by itself.

AND-parallelism

angelic nondeterminism

A notional ability always to choose the most favorable option, in constant time. With angelic non-determinism, any problem in NP would be solvable in polynomial time.

angelic semantics

answer literal

answer set programming

Answer set programming (ASP) is a form of declarative programming oriented towards difficult (primarily NP-hard) search problems. It is based on the stable model (answer set) semantics of logic programming.

answer sets

aortic coarctation

It is a Medical term which means the narrowing of aorta - the largest artery in the body which starts from the heart.

apprenticeship learning

It is the process of learning by observing the demonstration of an expert. In a way, it is a form of supervised learning where the training data would be the tasks performed by the expert.

architecture

It is a very broad term; however, it is generally used to refer to the structure of the buildings and other constructions.

arity

In logic, mathematics, and computer science, the arity of a function or operation is the number of arguments or operands that the function takes.

artificial life

Artificial life (often abbreviated ALife or A-Life) is a field of study wherein researchers examine systems related to natural life, its processes, and its evolution, through the use of simulations with computer models, robotics, and biochemistry.

ascending-bid

Bidders place bids of progressively higher amounts, aiming to outbid each other. The bidder who places the highest bid by the end of the auction wins.

Asilomar Principles

The Asilomar Conference on Beneficial AI was a conference organized by the Future of Life Institute, held January 5-8, 2017, at the Asilomar Conference Grounds in California. More than 100 thought leaders and researches in economics, law, ethics, and philosophy met at the conference, to address and formulate principles of beneficial AI. Its outcome was the creation of a set of guidelines for AI research – the 23 Asilomar AI Principles.

assignment

associative memory

In terms of Psychology, it is the type of memory which allows us to remember things by finding links between, apparently, unrelated things. To explain with an example; remembering someones' name by the dress they wore the first time you met them. Clearly, these two things seem completely unrelated.

asymptotic analysis

It is a Mathematical method of describing limiting behavior by using an input bound function, which means that the algorithm would run in constant time if no output was given, as the rest of the factors contributing to the computation are constant.

asymptotic bounded optimality

ATMS

Assumption-Based Truth Maintenance System (ATMS) allows to maintain and reason with a number of simultaneous, possibly incompatible, current sets of assumption.

atom

An atom is the smallest constituent unit of ordinary matter that has the properties of a chemical element.

atomic representation

atomic sentence

attribute-based extraction

augmented grammar

Any grammar whose productions are augmented with conditions expressed using features.

authority

automatic assembly sequencing

autonomy

It is the character of being independent and self-governing in vital as well as non-vital situaltions.

average reward

axiom

In Mathematics or logics, an axiom is a statement or a proposition which is assumed to be true to serve as a starting point for further arguments and reasoning. Example of an axiom: “Nothing can both be and not be at the same time and in the same respect”.

B

back-propagation

back-propagation is an algorithm used for supervised learning of artificial neural networks using gradient descent. The method calculates the gradient of a given error function with respect to the weights of the network. The "backward" terminology stems because the gradient calculation requires backward propagation through the newtork.

backed-up value

backgammon

Backgammon is one of the oldest known board games. It is a two player game where each player has fifteen pieces (checkers) which move between twenty-four triangles (points) according to the roll of two dice. The objective of the game is to be first to bear off, i.e. move all fifteen checkers off the board.

background subtraction

Foreground detection is one of the major tasks in the field of computer vision and image processing whose aim is to detect changes in image sequences. Background subtraction is any technique which allows an image's foreground to be extracted for further processing (object recognition etc.).

backjumping

backmarking

backoff model

backpropagation

To minimize the cost function, we need to know how the changes in weights and biases affect the cost function i.e. partial derivatives of the cost function w.r.t every weight and bias in the network; back propagation is a method that allows us to quickly compute all these partial derivatives.

Backus-Naur form (BNF)

A mathematical notation used to describe the syntax of a programming language.

backward-chaining

bag of words

The bag of words model is a simplifying representation used in natural language processing and information retrieval. Also known as the vector space model. In this model, a text is represented as the bag of its words, disregarding grammar and even word order but keeping multiplicity.

bagging

Bagging (stands for Bootstrap Aggregating) is a way to decrease the variance of your prediction by generating additional data for training from your original dataset using combinations with repetitions to produce multisets of the same cardinality/size as your original data. By increasing the size of your training set you can't improve the model predictive force, but just decrease the variance, narrowly tuning the prediction to expected outcome.

bang-bang control

baseline

batch gradient descent

Bayes' rule

Bayes' rule describes the probabilty of an event(lets say A) in the light of that a given event B has already occured. Mathematically Bayes' rule can be described as :- P(A|B) = P(A)P(B|A)/P(B)

Bayes-Nash equilibrium

Bayesian learning

A Machine Learning method which enables us to encode our initial perception of what a model should look like, regardless of what the data tells us. It proves to be very useful when there’s a sparse amount of data to train our model properly.

Bayesian network

A probabilistic graphical model representing a group of variables along with their conditional dependencies through a direct acyclic graph; it is also used to compute the probability distribution for a subset of network variables, provided the distributions or values of any subset of the remaining variables.

beam search

Beam search is a heuristic search algorithm that explores a graph by expanding the most promising node in a limited set. Beam search is an optimization of best-first search that reduces its memory requirements. Best-first search is a graph search which orders all partial solutions (states) according to some heuristic. But in beam search, only a predetermined number of best partial solutions are kept as candidates.[1] It is thus a greedy algorithm.

behaviorism

belief function

belief propagation

belief revision

belief state

Bellman equation

Bellman update

benchmarking

Benchmarking is to measure the quality of something for the purposes of comparison or evaluation.

best-first search

biconditional

binary constraint

binary resolution

binding list

binocular stereopsis

biological naturalism

blocks world

bluff

body

boid

Boids is an artificial life program, developed by Craig Reynolds in 1986, which simulates the flocking behaviour of birds. His paper on this topic was published in 1987 in the proceedings of the ACM SIGGRAPH conference. The name "boid" corresponds to a shortened version of "bird-oid object", which refers to a bird-like object.

boosting

Boosting is a two-step approach, where one first uses subsets of the original data to produce a series of averagely performing models and then "boosts" their performance by combining them together using a particular cost function (=majority vote). Unlike bagging, in the classical boosting the subset creation is not random and depends upon the performance of the previous models: every new subsets contains the elements that were (likely to be) misclassified by previous models.

boundary set

bounded optimality

bounded PlanSAT

bounded rationality

bounds consistent

bounds propagation

branching factor

bridge

bunch

C

calculative rationality

canonical distribution

cart-pole

A pole is attached by an un-actuated joint to a cart, which moves along a frictionless track. The system is controlled by applying a force to the cart in the left or right direction. The pendulum starts upright, and the goal is to prevent it from falling over. A reward is provided for every timestep that the pole remains upright.

cascaded finite-state transducers

case agreement

causal

causal link

causal network

center

central limit theorem

The central limit theorem (CLT) establishes that, in some situations, when independent random variables are added, their properly normalized sum tends toward a normal distribution (informally a "bell curve") even if the original variables themselves are not normally distributed.

certainty effect

certainty equivalent

CFG

chain rule

It is a mathematical formula used to compute the derivatives of a composition of two or more functions.

characters

A character is any letter, number, space, punctuation mark, or a symbol.

chart

checkers

chess

Chess is a two-player strategy board game played on a chessboard, a checkered gameboard with 64 squares arranged in an 8×8 grid. Play does not involve hidden information. Each player begins with 16 pieces: one king, one queen, two rooks, two knights, two bishops, and eight pawns. Each of the six piece types moves differently, with the most powerful being the queen and the least powerful the pawn. The objective is to checkmate the opponent's king by placing it under an inescapable threat of capture. To this end, a player's pieces are used to attack and capture the opponent's pieces, while supporting each other.

Chomsky Normal Form

A grammar is in Chomsky Normal Form (usually found as CNF) if all its production rules are in one of the following forms:

A -> BC
A -> a
S -> ε

where S is the starting symbol and ε the symbol for the empty string.

circuit verification

circumscription

Clark Normal Form

classification

Sorting or dividing data into two or more categories on the basis of a distinct feature.

clause

closed-loop

clustering

In terms of Data Science, clustering is the grouping of data instances or objects with similar features and characteristics.

clutter

CMAC

co-NP

co-NP-complete

coarse-to-fine

coarticulation

coastal navigation

cognitive psychology

It is a branch of Psychology which deals with the mental processes involved in obtaining and comprehending new information. Some of the prominent processes include judging, problem solving and remembering.

collusion

color constancy

communication

commutativity

competitive

competitive ratio

complementary literals

complete assignment

complete data

completeness

completing the square

completion

compliant motion

composition

compositional semantics

compositionality

computable

computational linguistics

computational neuroscience

conclusion

concurrent action list

conditional effect

conditional Gaussian

conditional probability table

conditional random field

conditioning

confirmation

conflict set

conflict-directed backjumping

conformant

conjugate gradient

conjunct ordering

conjunction

conjunctive normal form

connectionist

consciousness

consequentialism

consistency

consistent

consistent plan

A plan in which there are no cycles in the ordering constraints and no conflicts with the causal links.

constraint language

constraint learning

constraint logic programming

constraint optimization problem

constraint propagation

constraint satisfaction problem

constraint weighting

consumable

context-free grammar

context-specific independence

contingency plan

continuous

contraction

contradiction

control theory

controller

convention

convex set

convolution

A mathematical term which basically means merging two signals to form a third signal.

cooperative

coordination

corpus

Cournot competition

covariance

covariance matrix

critic

critical path

critical path method

cross-correlation

crossover point

cryptarithmetic

cumulative distribution

cumulative probability density function

current-best-hypothesis

cycle cutset

cyclic solution

CYK algorithm

D

DARPA Grand Challenge

The DARPA Grand Challenge is a prize competition for American Autonomous Vehicles,funded by the Defense Advanced Research Projects Agency,the most prominent research organization of the United States Department of Defense.

data association

data complexity

data compression

It is the process of encoding data using fewer bits than were used in the original representation so that the data consumes lesser disk space.

data matrix

data mining

data-driven

database semantics

Datalog

Davis-Putnam algorithm

decayed MCMC

decentralized planning

decision analysis

decision boundary

decision maker

decision network

decision theory

decision theory

decision tree

A decision tree is a construct that uses a tree like graph or model of decisions and their possible consequences,including chance event outcomes,resource costs and utility.

declarative

declarative bias

decomposition

deduction theorem

deductive learning

Going from a known general rule to a new rule that is logically entailed (and thus nothing new), but is nevertheless useful because it allows more efficient processing.

deep belief networks

deep learning

It is a subfield of Machine Learning that tries to map the working of the human brain in processing data and creating patterns to use in decision making.

default logic

definite clause

definite clause grammar

definition of a rational agent

deformable template

degree of belief

degree of freedom

It is a statistics term which represents the number of variables that you are allowed to change for analysis, without any constraint violation.

delete list

deliberative layer

demonic nondeterminism

Dempster-Shafer theory

depth

depth of field

depth-first search

It is an algorithm which allows us to traverse a graph or a tree data structure; it starts from the root node and traverses as far as possible for each branch before backtracking. (In case of graph data structure, the root node would be any arbitrary node that you select).

depth-limited search

detailed balance

Deterministic

diachronic

diagnostic

diameter

Differential GPS

diffuse albedo

Diophantine equations

direct utility estimation

Dirichlet process

disambiguation

discount factor

discrete

discretization

disjoint

disjunction

disjunctive constraint

disparity

distant point light source

distortion

distributed constraint satisfaction

DL

domain

domain closure

dominant strategy equilibrium

downward refinement property

dropping conditions

DT

dual graph

dualism

duration

dynamic

dynamic backtracking

dynamic Bayesian network

dynamic programming

A method of solving complex problems by breaking them down to sub-problems that can be solved by back tracking from the last stage. Used in popular real-world problems including traveling salesman problem, Fibonacci sequence, knapsack problem, etc.

dynamic state

E

early stopping

economy

effect

effective branching factor

efficient

electric motor

eliminative materialism

elitism

embodied cognition

emergent behavior

empirical gradient

empirical loss

empiricism

English auction

entailment

entropy

environment

environment generator

episodic

epsilon-ball

equality symbol

equilibrium

ergodic

error rate

event

evidence

evidence reversal

evolutionary algorithms

evolutionary psychology

evolutionary strategies

exact cell decomposition

execution

execution monitoring

executive layer

exhaustive decomposition

existence uncertainty

Existential Instantiation

expand

expectation

expectation-maximization

expected value

expectiminimax value

explanation-based learning

explanatory gap

exploitation

exploration

exploration problem

expressiveness

extended Kalman filter (EKF)

extension

extensive form

externalities

extrinsic

F

fact

factor

factored frontier

factored representation

factorial HMM

factoring

false negative

false positive

feature extraction

feature selection

feed-forward network

FIFO queue

filtering

finite horizon

first-choice hill climbing

first-order Markov process

fixate

fixed point

fixed-lag smoothing

flaw

fluent

focal plane

foreshortening

forward-backward algorithm

forward-chaining

frame problem

frames

framing effect

free space

frequentist

friendly AI

frontier

full joint probability distribution

fully observable

If an agent's sensors give it access to the complete state of the environment at each point in time,then we say that the task environment is fully observable.

functionalism

futility pruning

fuzzy control

A fuzzy control system is a control system based on fuzzy logic—a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 1 or 0 (true or false, respectively).

fuzzy logic

Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false.

fuzzy set theory

Fuzzy sets (aka uncertain sets) are somewhat like sets whose elements have degrees of membership. In classical set theory, the membership of elements in a set is assessed in binary terms according to a bivalent condition — an element either belongs or does not belong to the set. By contrast, fuzzy set theory permits the gradual assessment of the membership of elements in a set; this is described with the aid of a membership function valued in the real unit interval [0, 1].

G

G-set

gain parameter

gain ratio

gait

game theory

Game theory is the study of mathematical models of strategic interaction between rational decision-makers. It has applications in all fields of social science, as well as in logic and computer science. Originally, it addressed zero-sum games, in which one person's gains result in losses for the other participants. Today, game theory applies to a wide range of behavioral relations, and is now an umbrella term for the science of logical decision making in humans, animals, and computers.

game tree

Gaussian distribution

Gaussian error model

Gaussian filter

Gaussian process

generalization

generalization hierarchy

generalization loss

generalized modus ponens

generating

generator

genetic algorithms

genetic programming

Gibbs sampling

GLIE

global constraint

global minimum

Go

goal

goal clauses

goal formulation

goal monitoring

goal test

goal-directed reasoning

gold standard

gorilla problem

gradient

gradient descent

grammar

graph

graph coloring

grasping

greedy agent

greedy best-first search

grid world

ground term

grounding

H

Hamming distance

Hansard

haptic feedback

head

heavy-tailed distribution

Hebbian learning

Hessian

heuristic function

heuristic search

hidden Markov model

A hidden Markov model (or HMM) is a temporal probabilistic model in which the state of the process is described by a single discrete random variable.

hierarchical lookahead

hierarchical reinforcement learning

high-level action

Hinton diagrams

holdout cross-validation

homeostatic

homophones

horizon effect

Horn clause

hub

human-level AI

Hungarian algorithm

hybrid A*

hybrid agent

hybrid architecture

hybrid Bayesian network

hydraulic actuation

hypothesis

hypothesis prior

hypothesis space

I

i.i.d.

i.i.d. denotes independent and identically distributed random variables. They are defined on the same probability space, have identical probability distribution functions, and are mutually independent.

identification in the limit

identity matrix

identity uncertainty

ignore delete lists

ignore preconditions heuristic

image

imperfect information

implementation

implementation level

implication

importance sampling

inclusion-exclusion principle

incompleteness theorem

incremental belief-state search

independence

independent subproblems

index

indexed random variable

indexing

individuation

induction

inductive learning

Going from a set of specific input-output pairs to a (possibly incorrect) general rule is called inductive learning.

inductive logic

inductive logic programming

inference

inference rules

inferential frame problem

infinite

infinite horizon

infix

information extraction

information gain

information gathering

information retrieval

information sets

informed search

inheritance

initial state

input resolution

inside-outside algorithm

insurance premium

intelligence

interleaving

interlingua

internal state

interpretation

interreflections

intrinsic

intuition pump

inverse

inverse entailment

inverse kinematics

inverse reinforcement learning

inverted pendulum

inverted spectrum

IR

irreversible

iterative deepening search

iterative expansion

iterative-deepening A*

J

join tree

joint action

joint plan

JTMS

justification

K

k-d tree

K-means clustering

Kalman filtering

Kalman gain matrix

kernel

kernel function

kernel trick

kinematic state

kinematics

King Midas problem

knowledge acquisition

knowledge base

knowledge engineering

knowledge-based agents

Known

Kriegspiel

Kullback-Leibler divergence

L

label

Lambert's cosine law

landmarks

language

language generation

language identification

large-scale learning

layers

leak node

learning

An agent is learning if it improves its performance after making observations about the world.

learning curve

learning element

learning rate

least commitment

least-constraining-value

leave-one-out cross-validation

lens

level cost

level of abstraction

level sum

leveled off

lexical category

lexicon

LIFO queue

lifting lemma

likelihood

likelihood weighting

line search

linear Gaussian

linear interpolation smoothing

linear programming

linear regression

linear resolution

linear separator

linkage constraints

links

liquid event

Lisp

literal

local consistency

local search

locality

locality-sensitive hash

localization

locally structured

locally weighted regression

location sensors

locking

log likelihood

logic

logical equivalence

logical minimization

logical omniscience

logicist

logistic function

logistic regression

long-distance dependencies

LOOCV

loopy path

loosely coupled

loss function

lottery

low-dimensional embedding

M

machine reading

macrops

magic set

Mahalanobis distance

Maintaining Arc Consistency (MAC)

makespan

margin

marginalization

Markov blanket

Markov chain

Markov decision process

Markov localization

Markov network

Markov property

material value

materialism

matrix

max norm

max-level

maximin

maximin equilibrium

maximum a posteriori

maximum expected utility

maximum-likelihood

mechanism

mechanism design

mel frequency cepstral coefficient (MFCC)

memoization

memoized

mental states

mereology

metadata

metalevel learning

metalevel state space

metaphor

metareasoning

metonymy

micromort

min-conflicts

mind-body problem

minimax

minimax decision

minimax decision is the optimal choice which leads MAX to the state with the highest minimax value and leads MIN to lowest minimax value.

minimax search

minimax value

The minimax value of a node in a game tree is the utility (for MAX) of being in the corresponding state, assuming that both players play optimally from there to the end of the game.

minimum description length

minimum slack

minimum-remaining-values

Minkowski distance

missing precondition

missing state variable

mixture distribution

mixture of Gaussians

mobile manipulator

modal logic

model

model checking

model selection

modus ponens

monitoring

monotonic preference

monotonicity

Monte Carlo

Monte Carlo localization

Monte Carlo simulation

Monte Carlo tree search

motion blur

motion model

multiactor

multiagent

multiagent planning problem

multiagent systems

multiplexer

multiplicative utility function

multiply connected

multivariate Gaussian

multivariate linear regression

mutation

mutex

mutual preferential independence

mutually utility independent

myopic

N

n-armed bandit

n-gram model

natural kind

natural numbers

nearest-neighbor filter

The nearest-neighbour filter, which repeatedly chooses the closest pairing of predicted position and observation and adds that pairing to the assignment.

nearest-neighbors regression

negation

negative

neuroscience

Newton-Raphson

no-good

no-regret learning

noise

noisy channel model

noisy-OR

nondeterministic

nonholonomic

nonlinear

nonlinear regression

nonmonotonicity

nonparametric

nonparametric density estimation

nonparametric model

normalization

normalized form

normative theory

NP-complete

NP-completeness

null hypothesis

O

object model

objective function

objectivist

occupancy grid

occupied space

occur check

Ockham's razor

odometry

off-policy

omniscience

on-policy

online replanning

online search

ontological commitment

ontological engineering

ontology

open list

open-code

open-loop

operationality

operations research

optimal brain damage

optimal controllers

optimally efficient

optimization

optimizer's curse

optogenetics

ordering constraints

OR-parallelism

orientation

origin function

Othello

out of vocabulary

outcome

overall intensity

overfitting

P

PAC learning

PageRank

parameter independence

parameter learning

parametric model

Pareto dominated

parse tree

parsing

partial assignment

partial information

partial program

partially observable

particle filtering

partition

passive learning

A passive learning agent learns from its observations, but the actions the agent takes are not influenced by the learning process. This is in contrast to an active learning agent, which chooses actions that will facilitate its own learning.

path

path planning

paths

pattern matching

payoff function

PD controller

PDDL

Peano axioms

PEAS

peeking

percept

The term percept refers to the agent's perceptual inputs at any given instant.

percept schema

percept sequence

An agent's percept sequence is the complete history of everything the agent has ever perceived.

perception

perception layer

perceptron

perceptron network

perfect rationality

performance element

perplexity

persistence arc

persistent failure model

perspective projection

phone model

phoneme

phrase structure

physical symbol system

physicalism

piano movers

pictorial structure model

PID controller

plan monitoring

plan recognition

planning graph

PlanSAT

playout

ply

pneumatic actuation

point-to-point motion

poker

policy

policy evaluation

policy gradient

policy improvement

policy iteration

policy loss

policy search

policy value

polynomial kernel

pose

positive

possibility axiom

possibility theory

possible world

post-decision disappointment

pragmatics

precedence constraints

precision

Precision is a performance measure often used to describe some model, alongside other measures like accuracy, recall etc. Precision can be thought of as an efficiency measure of a model. It is given as:
Precision = True Positives / (True Positives + False Positives)

precondition

prediction

preference elicitation

preference independence

prefix

premise

presentation

principle of indifference

principle of insufficient reason

principle of trichromacy

prioritized sweeping

priority queue

prisoner's dilemma

probabilistic checkmate

probabilistic Horn abduction

probabilistic inference

probability

probability density function

probability distribution

probability model

probit distribution

problem

problem formulation

problem-solving agent

procedural attachment

process

product rule

progression planning

Prolog

pronunciation model

proof

proof-checker

proposition symbol

propositionalize

protein design

provably beneficial

pruning

psychological reasoning

PUMA

pure strategy

pure symbol

Q

Q-learning

QALY

quadratic programming

qualia

qualification problem

qualitative physics

quantification

quantization factor

quasi-logical form

question answering

queue

quiescence search

R

radial basis function

radiometry

random surfer model

random-restart hill climbing

randomized weighted majority algorithm

rational agent

A rational agent selects 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.

rationalism

rationality

reachable set

reactive control

reactive layer

real-time AI

realizable

reasoning

recall

Recall is a measure of performance used alongside Precision, Accuracy and F-score. It is defined as the ratio of the true positives to the summation of true positives and false negatives.

reciprocal rank

recognition

recombine

reconstruction

record linkage

rectangular grid

recurrent network

recursive

recursive best-first search

reduct

reference class

reference controller

reference path

reflect

reflective architecture

refutation

regions

regression

regression planning

regression to the mean

regret

regular expression

regularization

reinforcement

reinforcement learning

In reinforcement learning the agent learns from a series of reinforcements-rewards or punishments.

rejection sampling

relational extraction

relational uncertainty

relative error

relative likelihood

relaxed problem

relevance

relevance feedback

relevant

relevant-states

renaming

rendering

rendering model

repeated state

resolution

resolvent

result set

Rete algorithm

retrograde

reusable

revelation principle

revenue equivalence theorem

reward

reward shaping

reward-to-go

risk-averse

risk-neutral

risk-seeking

Robocup

robot navigation

robotic soccer

robust control theory

ROC curve

rollout

Roomba

Root Mean Square (RMS)

A mathematical formula, root mean square is often used as an error estimator. It is described as the root of the summation of all the squared errors. The formula is given as: <br>square_root[summation(a1^2 + a2^2 + ...)], where a1, a2...are some entities <br> In RMS error estimation, the above squared entities are replaced with squared errors.

rules

S

S-set

sample complexity

sample space

sampling rate

SARSA

SAT

satisfiability

satisfiability threshold conjecture

satisficing

scaled orthographic projection

scanning lidars

scene

schedule

schedulers

schema

Scrabble

sealed-bid second-price auction

search

search cost

search tree

segmentation

selection

semantic ambiguity

semantics

semi-supervised learning

semidynamic

semiotics

sensitivity analysis

sensor interface layer

sensor Markov assumption

sensorless

sequence form

sequential

sequential Monte Carlo

set of support

set semantics

set-cover problem

set-level

shading

shadow

shape

shaving

shortcuts

shoulder

sibyl attack

sideways move

sigmoid perceptron

significance test

similarity networks

simulated annealing

simultaneous localization and mapping (SLAM)

single agent

singly connected

singular

singularity

situation

situation calculus

skeletonization

Skolemization

Skolemization is the process of removing the existential quantifiers by elimination. This is similar to the inference rule Existential Elimination where an inference involving sentence a, variable v and constant k can be made provided k does not occur anywhere in the knowledge base.

slack

slant

sliding window

small-scale learning

smoothing

Smoothing is the process of computing the distribution over past states given evidence up to the present.

soccer

social laws

Socratic reasoner

soft margin

softmax function

The softmax function is a mathematical function often used for classification tasks. This function calculates the probability distribution of one class, over all the available classes. It's formula is given as:

F(X) = exp(X) / summation(exp(X)) with the summation being over all the classes.

software architecture

sokoban

solution

sonar sensors

sound

spam detection

sparse

sparse model

spatial reasoning

specialization

specular reflection

specularities

speech act

Speech Recognition

Speech recognition is the process of analyzing audio and recognizing parts related to speech within the audio file. This process of recognition may involve simply identifying the speech part, gender identification and also as complex as identifying the words spoken when given an audio. This field often overlaps into the domain of artificial intelligence and machine learning.

split point

stable

stable model

standard normal distribution

standardizing apart

Starcraft

start symbol

state abstraction

state estimation

state space

state-space landscape

static

stationarity assumption

stationary distribution

stationary process

stemming

step cost

step size

stereo vision

stochastic

stochastic beam search

stochastic games

stochastic hill climbing

stochastic policy

straight-line distance

strategic form

strategy

strategy profile

strategy-proof

strong AI

structural EM

structured representation

stuff

subcategory

subgoal independence

subject-verb agreement

subjectivist

subproblem

substitution

subsumption

subsumption architecture

subsumption lattice

successor

successor-state axiom

Sudoku

sum of squared differences

superpixels

supervised learning

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

support vector machine

symmetry-breaking constraint

synchro drive

synchronic

synchronization

syntactic ambiguity

syntactic theory

syntax

synthesis

T

table lookup

tabu search

tactile sensors

taxonomy

Taylor expansion

technological singularity

template

temporal logic

temporal-difference

temporal-projection

term

terminal states

terminal test

test set

text classification

texture

theorem proving

thrashing

tiling

tilt

time and tense

time line

time of flight camera

time to answer

tit-for-tat

topological sort

total Turing Test

toy problem

trace

tractability

tragedy of the commons

trail

training curve

training set

transfer model

transhumanism

transition model

transition probability

transpose

transposition table

traveling salesperson problem

tree decomposition

tree width

treebank

truth

truth value

truth-preserving

truth-revealing

turbo decoding

Turing Test

The Turing Test is a test proposed by Alan Turing in 1950, which is used to determine whether a computer is intelligent by evaluating the "human-ness" of its responses.

type A strategy

type B strategy

type constraint

type signature

U

ultraintelligent machine

unary constraint

unbiased

uncertainty

underconstrained

understanding

unification

unifier

uniform-cost search

Unimate

uninformed search

unique action axioms

unique string axiom

unit clause

unit preference

unit propagation

unit resolution

units function

universal grammar

Universal Instantiation

unknown

unobservable

unrolling

unsupervised clustering

unsupervised learning

In unsupervised learning the agent learns patterns in the input without any explicit feedback.

upper confidence bounds on trees

upper ontology

Urban Challenge

utility

utility independence

V

vague

validation set

A part of the dataset that is used to tune the parameters of a machine learning model. It can also be used to determine a stopping point for the back-propagation algorithm.

validity

value

value alignment

vanishing point

A vanishing point of a function means the function has a zero at the point or on the set.

variable

A variable is a symbol on whose value a function, polynomial, etc., depends.

variational approximation

variational parameters

VCG

vector

A vector is formally defined as an element of a vector space R^n. A vector is given by n coordinates and can be specified as (A_1,A_2,...,A_n).

vector field histograms

vehicle interface layer

verification

version space

Vickrey-Clarke-Groves

virtual counts

virtual support vector machine

VLSI layout

vocabulary

Voronoi graph

W

weak AI

weak learning

weight

weight space

weighted A* search

weighted training set

wide content

Widrow-Hoff rule

Winnow algorithm

workspace representation

wrapper

wumpus world

Z

zero-sum games

In game theory and economic theory, a zero-sum game is a mathematical representation of a situation in which each participant's gain or loss of utility is exactly balanced by the losses or gains of the utility of the other participants. If the total gains of the participants are added up and the total losses are subtracted, they will sum to zero.