Index

• & (base-level and) 14.4.1
• % (comment) 13.3
• $\leftarrow$ (if) 13.3, 5.3, 5.3, 5.1.1
• $\leftrightarrow$ (equivalence) 5.1.1
• $\Leftarrow$ (base-level if) 14.4.1
• $\mathrm{=}$ (equals) 13.7
• $\ne$ (not equal to) 13.7.2
• $\wedge$ (and) 5.3, 5.1.1
• $\mathrm{\vee }$ (or) 14.4.3
• $\mathrm{⊢}$ (prove) 5.3.2
• $\perp$ (bottom) 3.4
• $\mathrm{\vDash }$ (entails) 13.3.1, 13.3.2, 5.1.2
• $⟼$ (rewritten as) 13.6.1
• 0/1 error 7.2.1
• $A^{\mathrm{*}}$ search 3.6.1, 3.7.2
• abduction 15.1.2, 5.7, 5.7, Representation
• abductive diagnosis 5.7
• abilities 1.3
• ABox 14.3.2
• absolute error 7.2.1
• absorbing state 9.5
• abstractions 1.4.4
• achievement goal 1.5.7
• achievement goal 6.1.4
• action 2.2, 6.1
• action profile 11.2.1
• action constraint 6.4.1
• action features 6.4.1
• action function 3.2
• action instance 6.5
• action profile 11.4
• action replay 12.9.1
• action variable 6.4
• activation function 7.3.2, 7.5
• activation layer 7.5
• active learning Online and offline
• active sensor 2.4.4
• acts 1.1
• actuator 2.1, 2.2
• acyclic directed graph 3.3.1
• acyclic knowledge base 5.4.4, 5.6
• adaptive importance sampling 8.6.5
• additive independence 9.1.2
• additive utility 9.1.2
• adjective 13.6.6
• admissible heuristic Proposition 3.1, 3.6, 3.6.2
• admissible search algorithm 3.6.1
• aerodynamics 1.2.1
• agent 1.1, 1.3, 2.1, 2.2
  • purposive 1.3
• agent system 2.2
• AI 1.1
• AIspace 16.2
• algebraic variable 4.1.1
• algorithm
  • asynchronous value iteration Figure 9.17
  • back-propagation 4
  • boosting Figure 7.19
  • candidate elimination learner Figure 7.21
  • collaborative filtering Figure 15.5
  • crossover 29
  • CSP solver Figure 4.5
  • debugging
    • false positive Figure 5.6
    • missing answer Figure 5.7
  • decision tree
    • learning Figure 7.7
  • depth-first branch-and-bound search Figure 3.12
  • expectation maximization Figure 10.6
  • generalized arc consistency (GAC) Figure 4.3
  • genetic algorithm Figure 4.11
  • Gibbs sampling Figure 8.32
  • inductive logic programming Figure 15.2
  • iterative deepening Figure 3.8
  • k-means Figure 10.2
  • likelihood weighting Figure 8.30
  • linear layer (in neural network) 8
  • linear regression Figure 7.8
  • local search Figure 4.7
  • logistic regression Figure 7.10
  • minimax Figure 11.6
  • model-based reinforcement learning Figure 12.6
  • neural network 21
  • partial-order planning Figure 6.6
  • particle filtering Figure 8.31
  • policy hill-climbing Figure 12.8
  • policy iteration Figure 9.18
  • proof procedure
    • conflict, bottom-up Figure 5.9
    • conflict, top-down Figure 5.10
    • Datalog, top-down Figure 13.4
    • definite clause, bottom-up Figure 5.3
    • definite clause, top-down Figure 5.4
    • negation as failure, bottom-up Figure 5.11
    • negation-as-failure, top-down Figure 5.12
  • Q-learning Figure 12.3
  • SARSA Figure 12.5
  • SARSA with linear function approximation Figure 12.7
  • search Figure 3.4
  • sigmoid layer (in neural network) Figure 7.17
  • unification Figure 13.3
  • value iteration Figure 9.16
  • variable elimination
    • belief network Figure 8.10
    • CSP Figure 4.6
    • decision network Figure 9.13
    • single-stage decision network Figure 9.8
    • soft constraints Figure 4.12
• Allais Paradox 9.1.1
• alpha-beta ($\alpha$-$\beta$) pruning 11.3
• alternative (ICL) 15.3.1
• analysis 1.1
• Analytical Engine 1.2
• ancestor 5.4.4
• annealing 4.7.3
• annealing schedule 4.7.3
• answer 13.3.3, 5.3.1, 5.3.2, 5.5.2
• answer clause 5.3.2
• answer extraction 13.3.3, 13.4.4
• any-conflict algorithm 4.7.3
• anytime algorithm 1.5.4
• aperiodic Markov chain 8.5.1
• application of substitution 13.4.1
• applications of AI 1.1, 1.6, 16.2
• approximate inference Approximate inference, 8.6
• approximately correct 7.8.2
• approximately optimal solution Approximately optimal solution
• arc 3.3.1
• arc consistent 4.4, 4.9.1
• argument 13.3, 5.6.1
• Aristotelian definition 14.3
• array A.2
• Arrow’s impossibility theorem Proposition 11.1
• artificial general intelligence 16.1
• artificial intelligence 1.1
• ask 5.3.1, Step 4
• ask-the-user 5.4.2
• askable atom 5.4.2
• assertional knowledge base 14.3.2
• assignment 4.1.1
• assignment space 4.2
• assistive technology 16.2
• assumable 5.7, 5.5.2
• assumption-based truth maintenance system 5.10, 5.5.4
• asymptotic complexity 3.5.2
• asynchronous value iteration 9.5.2
  • algorithm Figure 9.17
• ATMS, see assumption-based truth maintenance system
• atom 5.3, 5.1.1
• atomic clause 13.3, 5.3
• atomic proposition 5.3, 5.1.1
• atomic symbol 13.3
• attribute 14.2.1
• auction 11.6
• autonomous agent 16.2
• autonomous delivery robot 1.6, 1.6.1
• autonomous vehicles 16.2
• average reward Average reward
• axiom Step 4, Step 3, 5.1.2
• axiom schema 13.7.1
• axiomatizing the domain Step 4, Step 3
• axioms of rationality 9.1
• axioms of probability 8.1.2
• back-propagation 7.5
  • algorithm 4
• background knowledge 1.4.1, 15.2.1
• backtracking 3.5.2, 4.3
• backward induction 11.3
• backward search 3.8.2
• bag-of-words 8.5.6
• bagging 7.6.1
• base language 14.4
• base learner 7.6.2
• base level 14.4
• base-level algorithms 7.6.2
• Basic Formal Ontology 14.6
• Basic Formal Ontology 14.3.3
• basic income 16.2
• batched gradient descent 7.3.2
• Bayes classifier 10.1.2
• Bayes’ rule 10.1, Proposition 8.4
• Bayesian information criteria (BIC) 10.1.4
• Bayesian learning 10.4
• Bayesian network, see belief network
• Bayesian probability 8.1
• beam search 4.8
• behavior 1.1
• belief 2.2.1
• belief monitoring 8.5.3
• belief network 10.3, 8.3, 8.3
  • causality 8.3.2
• belief state 1.3, 2.2.1, 2.4.2
• belief state transition function 2.2.1, 2.3
• best response 11.4
• best-first search 3.6
• beta distribution 10.4
• BFO, see Basic Formal Ontology
• bias 10.1, 7.4, Bias, 7.8.1
• bias-free hypothesis space 7.8.1
• bias–variance trade-off 7.4
• bidirectional search 3.8.2
• big data 7.4
• bigram model 8.5.6
• binary constraint 4.1.2
• binary variable 4.1.1
• binomial distribution Example 10.14
• biology Biology
• bit 7.2.1, 8.1.5
• blame attribution problem 12.1
• body
  • agent 2.1, 2.2
  • clause 13.3, 5.3
• Boltzmann distribution 12.5
• Boltzmann distribution 4.7.3, 4.8
• Boolean variable 5.2
• Boolean property 14.2.1
• Boolean variable 4.1.1
• boosting 7.6.2
  • algorithm Figure 7.19
• bot 2.1
• bottom $\mathrm{\perp }$ 3.4
• bottom-up proof procedure 5.3.2
• boundary of a process 14.3.3
• bounded above zero 3.6.1
• bounded rationality 1.5.4
• branch-and-bound search 3.8.1
• branching factor 3.3.1
• breadth-first search 3.5.1
• burn-in 8.6.7
• candidate elimination learner 7.8.1
  • algorithm Figure 7.21
• canonical form 9.1.2
• canonical representation 13.7.1
• cardinal feature 7.2.1
• cardinal preference 1.5.7
• case 7.7
• case analysis 4.5
• case-based reasoning 7.7
• causal link 6.5
• causal model 5.8
• causal network 8.3.2
• causal rule 15.1.1, 6.1.3
• causal transduction 2.2.1, 2.2.1
• causality 5.8, 5.8, 8.3.2
• chain rule 7.5, Proposition 8.3
• chance node 9.2.1
• characteristic function relations, 14.2.3
• children 3.3.1, 4.8
• choice space 15.3.1
• choose 3.4, 3.4, 5.3.2
• Church–Turing thesis 1.2
• clarity principle 8.3.2
• clarity principle 4.1.1, 8.3.2
• Clark normal form 13.8
• Clark’s completion 13.8, 5.6
• class 10.1.2, 10.2, 14.3.2, 14.2.3, 7.3.1
• classification Task, 7.3.1
• classification tree 7.3.1
• clause 5.2
  • definite 13.3, 13.3, 5.3
  • Horn 5.5.1
• closed list, see explored set
• closed-world assumption 5.6
• clustering 10.2
• CNF, see conjunctive normal form
• cognitive science 1.2.1
• cold-start problem 15.2.2
• collaborative filtering 15.2.2
  • algorithm Figure 15.5
• combinatorial auction 16.2
• command 12.4, 2.2, 2.2.1, 2.3
• command function 2.2.1, 2.3
• command trace 2.2.1
• commonsense reasoning 1.4.3, Example 1.2
• competitive agents 11.1
• complements 9.1.2
• complete
  • algorithm 3.7.3, 4.7
  • bottom-up derivation Completeness
  • knowledge assumption 5.6
  • linear layer 7.5
  • proof procedure 5.3.2
• completeness of preferences Axiom 9.1
• complex preference 1.5.7
• complexity 3.5.2
• compositional measure of belief 8.1.3
• compound proposition 5.1.1
• computational linguistics 13.6
• computational agent 1.1
• computational learning theory 7.8.2
• computational limits dimension 1.5.4
• computational sustainability 16.2
• concept 14.3.2
• conceptualization 13.2, 14.3
• conditional probability table 8.4.1
• conditional effect 6.1.2, 6.1.3
• conditional entropy 8.1.5
• conditional expected value 8.1.4
• conditional odds 8.4.2
• conditional probability 8.1.3
• conditional probability distribution 8.1.3
• conditionally independent 8.2
• conditioning 8.4.1
• Condorcet paradox Example 11.17
• conflict 4.7.1, 5.5.2
• conflicting variable 4.7.3
• conjugate prior Example 10.2
• conjunction 5.1.1
• conjunctive normal form Example 5.24
• consequence set 5.3.2
• consistency-based diagnosis 5.5.3, 5.5.3
• consistent heuristic 3.7.2
• consistent hypothesis 15.2.1, 7.8
• constant 13.3
• constrained optimization problem 4.9
• constraint 4.1.2, 4.1.2
• constraint satisfaction problem 4.1.3
• constraint network 4.4
• constraint optimization problem 4.9
• context-free grammar 13.6.1
• context-specific independence 8.4.2
• contingent attribute 14.2.3
• continuant 2, 14.3.3
• continuous time 8.5.5
• continuous variable 4.1.1
• controller 11.2.1, 2.2, 2.2.1
  • model-based reinforcement learning Figure 12.6
  • policy hill-climbing Figure 12.8
  • Q-learning Figure 12.3
  • SARSA Figure 12.5
  • SARSA with linear function approximation Figure 12.7
• convolutional neural network 7.5
• cooperate 12.10.2
• cooperative agents 11.1
• cooperative system Example 5.30
• coordinate 12.10.2
• coordination Example 11.12
• cost 3.3.1, 4.9
• CPT, see conditional probability table
• credit assignment problem 12.1, Feedback
• cross validation 7.4.3, 7.7
• crossover 4.8, 4.8
  • algorithm 29
• CSP, see constraint satisfaction problem
• CSP solver
  • algorithm Figure 4.5
• culture Culture
• cumulative probability distribution 8.6.1
• cumulative reward 9.5
• cut 7.2.1
• Cyc 14.3
• cycle 3.3.1
• cycle pruning 3.7.1
• cyclic knowledge base 5.4.4
• DAG (directed acyclic graph) 3.3.1
• Datalog 13.3
• datatype property 14.3.2
• DBN, see dynamic belief network
• DCG, see definite clause grammar
• dead reckoning 2.4.1
• debugging 5.4.4
  • false negative 5.4.4
  • false positive 5.4.4
    • algorithm Figure 5.6
  • incorrect answers 5.4.4
  • infinite loops 5.4.4
  • missing answer 5.4.4, 5.4.4
    • algorithm Figure 5.7
• decision
  • sequential 9.3, 9.3
  • single 9.2
• decision tree 9.2
• decision variable 9.2
• decision function 9.3.2
• decision network 9.3.1, 9.3.1
  • multiagent 11.2.3
  • single-stage 9.2.1
• decision node 9.2.1
• decision tree 10.3.1, 11.2.2, 7.3.1, 8.4.2
  • learning 10.1.3, 7.3.1, 8.1.5
    • algorithm Figure 7.7
• decision-theoretic planning 9.5.4
• deduction 5.3.2, 5.7, Representation
• deep learning 1.5.10, 7.5, 7.5
• deep reinforcement learning 12.9.1
• Deep Space One 10.
• default 5.4.2, 5.6.1
• definite clause 13.3, 13.3, 5.3
• definite clause grammar (DCG) 13.6.1
• definite clause resolution 5.3.2
• delay 13.7.2, 14.4.6
• delivery robot 1.6.1
• DENDRAL 1.2
• denote 13.3.1
• dense time 2.2.1
• dependent continuant 12, 14.3.3, 14.3.3
• depth bound 3.5.3
• depth-bounded search 3.5.3
• depth-bounded meta-interpreter Figure 14.12
• depth-bounded search
  • algorithm 12
• depth-first branch-and-bound search 3.8.1
  • algorithm Figure 3.12
• depth-first search 3.5.2
• derivation 13.4.4, 5.3.2, 5.3.2
• derived 5.3.2
  • atom 5.8
  • feature 6.1.2
  • knowledge 14.2.3
  • relation 15.1.1
• description logic 14.3.2
• descriptive theory 9.1.3
• design 1.1, 5.7
• design space 1.5
• design time computation 1.4.1, 2.4.3
• desire 2.2.1
• deterministic 1.5.6
• diagnosis 5.7
  • abductive 5.7
  • consistency-based 5.5.3
  • decision theoretic Example 9.8
• diagnostic assistant 1.6, 1.6.2
• dictator 11.6
• dictionary 8.6.6
• difference list 13.6.1
• differentia 14.3
• dimension
  • computational limits 1.5.4
  • effect uncertainty 1.5.6
  • interaction 1.5.9
  • learning 1.5.5
  • modularity 1.5.1
  • number of agents 1.5.8
  • planning horizon 1.5.2
  • preference 1.5.7
  • representation 1.5.3
  • sensing uncertainty 1.5.6
• directed graph 3.3.1
• directed acyclic graph 3.3.1
• Dirichlet distribution 10.4
• discount factor Discounted reward
• discounted reward Discounted reward
• discrete time 2.2.1
• discrete variable 4.1.1, 8.1.1
• discretization 8.1.1, 8.5.5
• disjunction 14.4.3, 5.1.1, 5.5.1
• disjunctive normal form Example 5.24
• disposition 19, 14.3.3
• DNF, see disjunctive normal form
• do command 12.4, 15.1.1, 2.3
• document 8.5.6
• domain functions, 13.3.1, 14.2.1, 4.1.1, 7.2, 8.1.1, 8.1.1
• domain splitting 4.5
• domain consistent 4.4
• domain expert 2.4.3
• domain ontology 14.3.2
• dominant strategy 11.6, 11.4.1
• don’t-care non-determinism 3.4
• don’t-know non-determinism 3.4
• dot product 8.5.3
• DPLL 5.2.1
• dynamic belief network 8.5.4
• dynamic decision network 9.5.4, 9.5.4
• dynamic programming 3.8.3
• dynamic relation 15.1.1
• dynamics 1.5.6, 8.5.1, 9.5
• economically efficient mechanism 11.6
• effect 6.1.2, 6.1
• effect constraint 6.4
• effect uncertainty dimension 1.5.6
• effectively computable function 1.2
• effector 2.1
• elimination ordering 8.4.1
• EM, see expectation maximization
• embodied agent 2.1
• empirical frequency 7.2.3
• empirical systems 1.1
• empty body 13.3, 5.3
• engineering goal 1.1
• ensemble learning 7.6.2
• entails 5.1.2
• entity 1.5.3, 13.1, 14.3.3
• entropy 7.2.1, 7.2.1, 8.1.5
• environment 1.3
• epistemology 1.2.1, 8.1
• equal 13.7
• equilibrium distribution 8.5.1
• equivalence 5.1.1
• ergodic Markov chain 8.5.1
• error 7.2.1, 7.5
  • 0/1 7.2.1
  • absolute 7.2.1
  • data Noise
  • false negative 5.4.4, 7.2.2
  • false positive 5.4.4, 7.2.2
  • sum squares 7.2.1
  • worst case 7.2.1
• error layer 7.5
• error of hypothesis 7.8.2
• Euclidean distance 3.7.2, 7.7
• evaluation function 11.3, 4.7.1
• event calculus 15.1.2
• evidence 8.1.3
• evidential model 5.8
• evolutionary algorithm 12.2
• exact inference Exact inference
• exclusive-or Example 7.12
• existence uncertainty 15.3.1
• existentially quantified variable 13.3.2
• expanding a path 3.4
• expectation 10.2.2
• expectation maximization 10.2.2, 10.3.2
  • algorithm Figure 10.6
• expected monetary value 9.1.1
• expected utility 9.3.2
• expected value 8.1.4
  • of utility of a policy 9.3.2
• experience 12.4
• expert system 1.3
• expert opinion 10.1.1
• expert system 1.2, 2.4.2
• explained away Example 5.34, Example 8.15
• explanation 5.7
• explanation-based learning 14.4.6
• exploit 12.5
• explore 12.5
• explore probability 12.5
• explore–exploit dilemma 12.1
• explored set 3.7.2
• expression 13.3
• extension 4.1.2, Example 4.7, 5.1
• extensional set 14.2.3
• extensive form 11.2.2
• external knowledge source 2.4.4
• extrapolation Interpolation and extrapolation
• $f(p)$ 3.6.1
• fact 13.3, 5.3
• factor A.2, 8.4.1, 8.4.2
• factored finite state machine 2.2.1
• factored representation 2.2.1
• factorization 8.3
• failure 3.4, 5.6.2
  • natural 3.5.3
• fairness 1.4.1, 13.5.1, 3.4
• false 13.3.1, 5.1.2
• false-negative error Probable solution, 5.4.4, 5.4.4, 7.2.2
• false-positive error Probable solution, 5.4.4, 5.4.4, 7.2.2
• false-positive rate 7.2.2
• fault 5.5.3
• feature 1.5.3, Chapter 4, 7.2
• feature engineering 12.9.1
• feature engineering 12.9
• feature selection 7.4.2
• feature-based representation of actions 6.1.3
• feed-forward neural network 7.5
• FIFO 3.5.1
• filtering 2.4.1, 8.5.2, 8.5.3
• finite failure 5.6.2
• finite horizon 1.5.2
• finite state controller 2.2.1
• finite state machine 2.2.1
• first-order predicate calculus 13.5
• fixed point Fixed Point
• flat structure 1.5.1
• floundering goal 13.8.1
• fluent 15.1.1
• flying machines 1.2.1
• foaf (friend-of-a-friend) Example 14.12
• fold 7.4.3
• for all ($\mathrm{\forall }$) 13.3.2
• forward chaining 5.3.2
• forward planner 6.2
• forward sampling 8.6.2
• forward search 3.8.2
• found a solution 3.4
• frame constraint 6.4
• frame problem 15.5
• frame rule 15.1.1, 6.1.3
• framing effect 9.1.1
• fringe 3.4
• frontier 3.4
• fully observable Markov decision process (MDP) 9.5
• fully observable world 1.5.6
• function 21, functions, 14.3.3
  • symbol 13.5
• functional gradient boosting 7.6.2
• fuzzy terms 2.3
• gambling 8.1
• game tree 11.2.2
• general boundary 7.8.1
• general game playing 11.8
• general-to-specific search 15.2.1
• generalization Measuring success
• generalized additive independence 9.1.2
• generalized answer clause 13.4.4
• generalized arc consistency (GAC) 4.4
  • algorithm Figure 4.3
• generate and test 4.2
• generate and test 4.9.1
• generic search algorithm 3.4
• genetic algorithm 4.8
  • algorithm Figure 4.11
• genus 14.3
• Gibbard–Satterthwaite theorem 11.6
• Gibbs distribution 12.5, 4.7.3, 4.8
• Gibbs sampling 8.6.7
  • algorithm Figure 8.32
• global optimum 4.7.1, 4.9.2
• goal 1.5.7, 1.3, 2.2.1, 3.2, 3.3.1, 6.1.4, 6.2
• goal node 3.3.1
• goal constraint 6.4
• goal state 3.2
• Google 8.5.1, Example 8.37
• gradient descent 4.9.2, 7.3.2
• grammar 13.6.1
  • context-free 13.6.1
  • definite clause 13.6.1
• granularity 8.5.5
• graph 3.3.1
• greedy 1.5.2
• greedy look-ahead 15.2.1
• greedy ascent 4.7.1
• greedy best-first search 3.6
• greedy descent 4.7.1
• greedy optimal split 7.3.1
• ground instance 13.4.1
• ground expression 13.3
• ground instance 13.4.2
• ground representation 14.4.1
• ground truth 7.4
• guaranteed bounds Approximate inference
• $h(n)$, see heuristic function
• hard clustering 10.2
• hard constraint Chapter 4, 4.1.2
• head 13.3, 5.3
• help system Example 10.5, Example 8.35
• Herbrand interpretation 13.4.2
• heuristic 3.4
• heuristic depth-first search 3.6
• heuristic function 3.6
• heuristic knowledge 3.1
• heuristic search 3.6
• hidden Markov model 9.5
• hidden Markov model (HMM) 8.5.2
• hidden property 15.2.2
• hidden units 7.5
• hidden variable 10.2.2, 10.3.2, 8.3.2
• hierarchical control 2.3
• hierarchical structure 1.5.1
• hill climbing 4.7.1
• history 1.3, 12.4, 2.2.1
• HMM, see hidden Markov model
• Hoeffding’s inequality 8.6.1
• horizon 1.5.2, 2.3, 6.4
• Horn clause 5.5.1
• how question 5.4.3
• how question 14.4.5
• hybrid system 2.3
• hyperplane 7.3.2
• hypothesis space 7.8
• i.i.d., see independent and identically distributed
• identity uncertainty 15.3.1
• immaterial entity 10, 14.3.3
• imperfect information 9.4
• imperfect-information game 11.2.2, 11.4
• implication 5.1.1
• importance sampling 8.6.4, 8.6.5
• incoming arc 3.3.1
• inconsistent 5.5.1
• incorrect answer 5.4.4
• incremental gradient descent 7.3.2
• indefinite horizon 1.5.2, 9.5
• independent and identically distributed (i.i.d.) 10.4
• independent choice logic (ICL) 15.3.1
• independent continuant 4, 14.3.3
• independent variables 8.2
• indicator variable 5.2, 7.2.1
• indifferent 9.1.1
• individual 1.5.3, 13.1, 13.3.1, 14.3.2, 4.8
• individual–property–value triple 14.2.1
• induction 5.7, Representation
• inductive logic programming 15.2.1, 15.2.1
  • algorithm Figure 15.2
• inference 5.3.2
• infinite horizon 1.5.2, 9.5
• influence diagram 9.3.1
• information gain 7.2.1
• information theory 10.1.4
• information content 7.2.1, 8.1.5
• information gain 7.3.1, 8.1.5
• information seeking actions 9.3
• information set 11.2.2
• information theory 7.2.1, 8.1.5
• inheritance 14.2.3
• $init$ (initial situation) 15.1.1
• initial part of a path 3.3.1
• initial-state constraint 6.4
• input features 7.2
• input layer 7.5
• insects 1.4.1
• instance 13.3.3, 13.4.1, 13.4.1
  • ground 13.4.2
• instance space 7.8
• insurance Example 9.1
• integrity constraint 5.5.1
• intelligent action 1.1
• intelligent tutoring system 1.6.3
• intended interpretation Step 3, 13.2, 5.1.2
• intension 4.1.2, Example 4.7, 5.1
• intensional set 14.2.3
• intention 2.2.1
• interaction dimension 1.5.9
• interoperate 14.1
• interpolation Interpolation and extrapolation
• interpretation 13.3.1, 5.1.2
• intersection A.3
• intervention 5.8, 8.3.2
• inverse graph 3.8.2
• inverse reinforcement learning 16.2
• involve a variable 4.1.2
• is (Prolog predicate) Example 13.37, 14.4.4
• is_a Example 14.5
• island 3.8.2
• island-driven search 3.8.2
• iterative soft-thresholding 7.4.2
• iterative best improvement 4.7.1
• iterative deepening 3.5.3
  • $A^{\mathrm{*}}$ (${\mathrm{\text{IDA}}}^{\mathrm{*}}$) 3.6.1
  • algorithm Figure 3.8
• Java 13.2, 14.2.3
• join A.3
• joint probability distribution 8.1.1
• joint probability distribution 8.3
• $k$-fold cross validation 7.4.3
• k-means 10.2.1
  • algorithm Figure 10.2
• kd-tree 7.7
• kernel function 7.6, 7.6
• knowledge 1.4.2, 2.4.2
• knowledge base 2.4.2
• knowledge base 1.4.1, 1.4.2, 13.3, 13.3, 2.4.2, 5.3, 5.1.2
• knowledge base designer 5.1.2
• knowledge engineer 2.4.3
• knowledge graph 14.2.2
• knowledge is given 1.5.5
• knowledge is learned 1.5.5
• knowledge level 1.4.4, 5.4.3
• knowledge-based system 2.4.2
• knowledge-level debugging 5.4.4
• $L_{\mathrm{0}}$ error 7.2.1
• $L_{\mathrm{1}}$ error 7.2.1
• $L_{\mathrm{1}}$ regularizer 7.4.2
• $L_{\mathrm{2}}$ regularizer 7.4.2
• $L_{\mathrm{2}}$ error 7.2.1
• $L_{\mathrm{\infty }}$ error 7.2.1
• landmark 2.3
• language 13.6.1
  • natural 13.6, 8.5.6
• language bias 7.8.1
• Laplace smoothing 10.4, Example 7.18
• Laplace smoothing 10.1.1
• latent tree model 10.1.2
• latent variable, see hidden variable
• law of large numbers 8.6.1
• Laws of Robotics 16.2
• layer (neural network) 7.5
• leaf 3.3.1
• learning Chapter 10—10.7, Chapter 12—12.13, 15.2—15.2.2, 2.4.2, Chapter 7—7.11
  • Bayesian 10.4
  • belief network 10.3—10.3.5
  • bias 10.1, 7.4, Bias, 7.8.1
  • case-based 7.7
  • collaborative filtering 15.2.2
  • decision tree 10.1.3, 7.3.1, 8.1.5
  • deep 7.5
  • expectiation maximization (EM) 10.2.2, 10.3.2
  • inductive logic programming 15.2.1
  • k-means 10.2.1
  • minimum description length 10.1.4
  • missing data 10.3.3
  • multiagent 12.10.2
  • naive Bayes classifier 10.1.2
  • neural network 7.5
  • PAC 10.4, 7.8.2
  • probabilistic classifier 10.1.2
  • probabilities 10.1, 10.3.1
  • reinforcement Chapter 12
  • relational 15.2
  • structure 10.3.4
  • supervised Feedback
  • to coordinate 12.10.2
  • to act Chapter 12
  • unsupervised 10.2, Feedback
  • version space 7.8.1, 7.8.1
• learning dimension 1.5.5
• learning rate 7.3.2
• least fixed point Fixed Point
• leave-one-out cross validation 7.7
• leave-one-out cross validation 7.4.3
• level of abstraction 1.4.4
• lifelong learning Lifelong learning
• LIFO 3.5.2
• likelihood 8.1.3
• likelihood of data 10.1, 7.2.1
• likelihood ratio 8.4.2
• likelihood weighting 8.6.4
  • algorithm Figure 8.30
• linear classifier 7.3.2
• linear function 7.3.2
• linear layer (in neural network)
  • class 8
• linear programming 4.9
• linear regression 12.9.1, 7.3.2
  • algorithm Figure 7.8
• linear rule for differentiation 7.5
• linearly separable 7.3.2
• Linnaean taxonomy 14.3
• list Example 13.29
• literal 5.2, 5.6
• liveness 1.4.1
• local search 4.7
• local optimum 4.7.1, 4.9.2
• local search 4.7
  • algorithm Figure 4.7
• localization 8.5.2
• log loss 7.2.1, 7.3.2
• log-likelihood 7.2.1
• log-linear model 8.4.2
• logic 13.2
• logic program 13.5
• logic program 5.10
• logical variable 13.3
• logical connectives 5.1.1
• logical consequence 13.3.1, 13.3.2, 5.1.2
• logical form 13.6.6
• logical formula 5.1.1
• logically follows 5.1.2
• logistic function 7.3.2
• logistic regression 10.1.2, 10.3.1, 7.3.2, 8.4.2
  • algorithm Figure 7.10
• long-term memory 2.4.2
• loop pruning 3.7.1
• lottery 9.1.1
• lowest-cost-first search 3.5.4, 3.5.4
• machine learning, see learning
• maintenance goal 1.5.7, 6.1.4
• MAP model 10.1
• mapping functions
• margin 7.6
• Markov decision process 12.1
• Markov assumption 8.5.1
• Markov blanket 8.3, 8.6.7
• Markov chain 8.5.1
• Markov chain Monte Carlo 8.6.7
• Markov decision process 9.5, 9.5—9.5.5
• material entity 6, 14.3.3
• matrix multiplication 8.5.3
• matrix factorization Add $k$ hidden properties
• maximization 10.2.2
• maximum a posteriori probability 10.1
• maximum entropy Maximum entropy or random worlds, 8.2
• maximum likelihood model 10.1, 7.2.1
• maximum-likelihood estimate 7.2.3
• MCMC, see Markov chain Monte Carlo
• MDL, see minimum description length
• MDP, see Markov decision process
• mean 2.
• measure 8.1.1, 8.1.1
• mechanism 11.1, 11.6
• mechanism design 11.6
• median 3.
• memory 2.2.1
• meta-interpreter 14.4, 14.4, 14.4.2
  • build proof tree Figure 14.13
  • built-in predicates Figure 14.11
  • delayed goals Figure 14.14
  • depth-bounded Figure 14.12
  • disjunction Figure 14.11
  • vanilla Figure 14.9
• meta-level 14.4
• metalanguage 14.4
• MGU, see most general unifier
• min-factor elimination ordering 4.6
• minimal conflict 5.5.2
• minimal diagnosis 5.5.3
• minimal explanation 5.7
• minimal model 13.4.2, Fixed Point
• minimax 11.3, 12.10.1
  • algorithm Figure 11.6
• minimum deficiency elimination ordering 4.6
• minimum description length (MDL) 10.1.4
  • MDL principle 10.1.4
• missing at random 10.3.3
• missing data 10.1.2, 10.3.3, Noise
• mode 1.
• model 1.4.4, 13.3.1, 2.4.1, 4.1.2, 5.1.2
• model averaging 10.4
• model complexity 7.4
• model-based reinforcement learning
  • algorithm Figure 12.6
• modular structure 1.5.1
• modularity 1.5.1, 1.5.1
• modus ponens 5.3.2
• modus ponens 5.3.2
• money pump 9.1.1
• monitoring 8.5.2, 8.5.3
• monotone restriction 3.7.2
• monotonic logic 5.6.1
• moral graph 8.4.1
• more general hypothesis 7.8.1
• more specific hypothesis 7.8.1
• most general unifier 13.4.1, 13.4.3
• most improving step 4.7.3
• multiagent decision network 11.2.3
• multiagent reasoning 1.5.8, Chapter 11—11.9
• multiple-path pruning 3.7.2, 3.7.2
• mutex constraint 6.4.1
• MYCIN 1.2
• myopic 1.5.2
• myopically optimal split 7.3.1
• n-gram 8.5.6
• naive Bayes classifier 10.1.2, 10.2.2, Example 8.35
• Nash equilibrium 11.4
• natural kind 10.1.2, 10.1.2, 14.2.3
• natural language processing 8.5.6
• natural language processing 13.6
• nature 1.5.8, 1.3, 11.1
• nearest neighbors 7.7
• negation 5.1.1, 5.5.1
• negation as failure 5.6
• negation as failure 13.8, 13.8, 5.6.2
• negative examples 15.2.1
• negative income tax 16.2
• neighbor 3.3.1
• Netflix Prize 15.2.2
• neural network 10.3.1, 7.5—7.5
  • algorithm 21
• neuron 1.2, 7.5
• no answer 5.3.1
• no-forgetting agent 9.3.1
• no-forgetting decision network 9.3.1
• node 3.3.1
• noise 7.4, Noise
• noisy observation 8.5.2
• noisy-or 8.4.2, Example 8.37
• non-deterministic choice 3.4
• non-deterministic procedure 3.4
• non-ground representation 14.4.1
• non-monotonic logic 5.6.1
• non-parametric distribution 8.1.1
• non-planning agent 1.5.2
• non-serial dynamic programming 4.11
• non-terminal symbol 13.6.1
• nonlinear planning 6.5
• norm 7.2.1
• normal form game 11.2.1
• normative theory 9.1.3
• noun 13.6.6
• $NP$ 3.4
• $NP$-complete 3.4
• $NP$-hard 3.4
• $\mathrm{\#}NP$ (sharp-NP) 8.4
• number of agents dimension 1.5.8
• number uncertainty 15.3.1
• object 8, 1.5.3, 13.1, 13.6.6, 14.2.1, 14.3.3
• object language 14.4
• object property 14.3.2
• object-oriented languages 13.2, 14.2.3
• objective function 4.9
• observation 1.3, 1.4.1, 5.4.1, 8.1.3, 8.3.2
• occurrent 23, 14.3.3, 14.3.3
• occurs check 13.5.1
• occurs check 13.5.1
• Ockham’s razor 7.1
• odds 8.4.2
• off-policy learner 12.7
• offline computation 1.5.9, 1.4.1, 2.4.2, 2.4.3
• offline learning Online and offline
• offline reasoning, see offline computation
• omniscient agent 4.1.1
• on-policy learner 12.7
• one-point crossover 4.8
• online computation 1.5.9, 1.4.1, 2.4.2, 2.4.4
• online learning Online and offline
• online reasoning, see online computation
• ontology 14.1, 14.3, 2.4.3, 5.1.2, 5.4.1, 8.1
• open-world assumption 5.6
• optimal algorithm 3.8.3
• optimal policy 3.8.3, 9.2.1, 9.3.2, 9.5.1
• optimal solution Optimal solution, Optimal solution, 3.2, 3.3.1, 3.5.4
• optimality criterion 4.9
• optimism in the face of uncertainty 12.5
• optimization problem 4.9
• oracle 3.4
• orders of magnitude reasoning 2.3
• ordinal preference 1.5.7
• ordinal feature 7.2.1
• organizations 1.1.1, 16.2
• outcome 11.2.1, 9.1.1, 9.2
• outgoing arc 3.3.1
• outlier Example 7.4
• overconfidence 7.4
• overfitting 7.3.1, 7.4
• OWL 14.3, 14.3.2
• OWL functional-style syntax 14.3.2
• PAC learning 10.4
• PAC learning 7.8.2
• Pagerank 8.5.1
• pair tuples
• parameterized random variable 15.3.1
• parametric distribution 8.1.1
• paramodulation 13.7.1
• parents 8.3
• partial evaluation 14.4.6
• partial observation 8.5.2
• partial restart 4.7.5
• partial-order planning 6.5
  • algorithm Figure 6.6
• partially observable game 11.2.2
• partially observable game 11.4
• partially observable Markov decision process 9.5, 9.5.5
• partially observable world 1.5.6
• particle 8.6.6
• particle filtering 8.6.6
  • algorithm Figure 8.31
• partition function 10.4
• partition function 10.1
• passive sensor 2.4.4
• past experience 1.3
• path 3.3.1
• path consistency 4.4
• pattern database 3.6.2, 3.8.3
• payoff matrix Example 11.1
• percept 12.4, 2.2
• percept function 2.3
• percept stream 2.2.1
• percept trace 2.2.1
• perceptron 1.2, 7.3.2
• perfect information 11.3
• perfect rationality 1.5.4
• perfect-information game 11.2.2, 12.10.1
• periodic Markov chain 8.5.1
• personalized recommendations 15.2.2
• philosophy 1.2.1
• physical symbol system 1.4.4
• physical symbol system hypothesis 1.4.4, 7.5
• pixel 2.2
• plan 6.2
• planner 6.2
• planning 15.1.1, 6.2—6.8
  • CSP 6.4
  • forward 6.2
  • partial-order 6.5
  • regression 6.3
• planning horizon 2.3
• planning horizon 1.5.2
• planning horizon dimension 1.5.2
• plate 15.3.1
• plate model 15.3.1
• point estimate 7.2.1
• policy 3.8.3, 9.2.1, 9.3.2, 9.5.1
  • stochastic 12.10.2
• policy hill-climbing 12.10.2
  • algorithm Figure 12.8
• policy iteration 9.5.3
  • algorithm Figure 9.18
• policy search 12.2
• POMDP, see partially observable Markov decision process
• population 4.8, 8.6.6
• positive examples 15.2.1
• possible action 6.3
• possible world 15.3.1, 4.1.1, 4.1.1, 8.1.1, 9.2, 9.3.2
• posterior distribution 8.3.1, 8.4
• posterior probability 8.1.3, 8.1.3
• pragmatics Pragmatics, 13.6.7
• precision 7.2.2
• precision-recall space 7.2.2
• precondition 15.1.1, 6.1.2, 6.1, 6.1.2
• precondition constraint 6.4
• predicate symbol 13.3
• prediction error 7.2.1
• predictor 7.2.2
• preference 1.3, 4.9
• preference elicitation 9.1.2
• preference bias 7.8.1
• preference dimension 1.5.7
• preposition 13.6.6
• primitive
  • atom 5.8
  • feature 6.1.2
  • knowledge 14.2.3
  • relation 14.3, 15.1.1
• primitive proposition 8.1.1
• prior probability 8.1.3
• prior count 10.1.1, 7.4.1
• prior knowledge 1.3
• prior odds 8.4.2
• prior probability 10.1, 8.1.3
• prisoner’s dilemma 11.4
• privacy 15.2.2
• probabilistic independence 8.2
• probabilistic bounds Approximate inference
• probabilistic inference 8.4
• probabilistic relational model 15.3.1
• probabilistically depends on 8.3
• probability 7.2.3, 8.1—8.9
  • axioms 8.1.2
  • Bayes’ rule 10.1, Proposition 8.4
  • conditional 8.1.3
  • density function 8.1.1
  • distribution 8.1.1
  • measure 8.1.1, 8.1.1
  • posterior 8.1.3
  • prior 8.1.3
  • semantics 8.1.1
• probable solution Probable solution, Probable solution
• probably approximately correct 7.8.2
• probably approximately correct 8.6.1
• probably approximately correct learning 7.8.2
• process 26, 1.5.2, 14.3.3
• process boundary 28
• projection A.3
• Prolog 1.2, 13.3
• proof 5.3.2
• proof procedure 5.3.2
  • bottom-up 5.3.2
  • conflict, bottom-up
    • algorithm Figure 5.9
  • conflict, top-down
    • algorithm Figure 5.10
  • Datalog, top-down
    • algorithm Figure 13.4
  • definite clause, bottom-up
    • algorithm Figure 5.3
  • definite clause, top-down
    • algorithm Figure 5.4
  • negation as failure, bottom-up
    • algorithm Figure 5.11
  • negation-as-failure, top-down
    • algorithm Figure 5.12
  • top-down 5.3.2
• proof tree 14.4.5, 5.4.3
• $\mathrm{p}\mathrm{r}\mathrm{o}\mathrm{p}$ Example 14.4
• property 1.5.3, 13.1, 14.3.2, 14.2.1, 14.2.1, 14.3.3
• property inheritance 14.2.3
• proposal distribution 8.6.5
• proposition 1.5.3, 13.1, 5.1.1, 5.1.1
• propositional satisfiability 5.2
• propositional calculus 5.1
• propositional definite clause 5.3
• propositional definite clause resolution 5.3.2
• prospect theory 9.1.1, 9.1.3
• Protégé 14.3.2
• proved 5.3.2
• pruning belief network 8.4.1
• pseudocount 10.1.1, 10.1.2, 10.4, 7.4.1, 7.4.2
• psychology 1.2.1
• punishment 9.5
• pure literal 5.2.1
• pure strategy 11.4
• purposive agent 1.3
• purposive agent 2.1
• Python 14.2.3
• $Q^{\pi }$ 9.5.1
• $Q^{*}$ 12.4, 9.5.1
• Q-learning 12.4
  • algorithm Figure 12.3
• Q-value 9.5.1
• qualitative derivatives 2.3
• qualitative reasoning 2.3
• quality 15, 14.3.3
• quantitative reasoning 2.3
• query 13.3, 13.3, 5.3.1
• query variable 8.3.1
• querying the user 5.4.2
• queue 3.5.1
• random restart 10.2.1
• random variable 8.1.1
• random worlds Maximum entropy or random worlds
• random forest 7.6.1
• random initialization 4.7
• random restart 4.7.2, 4.7, 4.7.5
• random sampling 4.7
• random walk 4.7.2, 4.7
• random worlds 8.2
• range functions, 14.2.1
• rational 9.1.1
• rational agent 9.1
• RDF 14.3
• RDF–S 14.3
• reachable state 15.1.1
• reactive system 2.4.1
• recall 7.2.2
• receiver operating characteristic 7.2.2
• recognition 5.7
• recommender system 15.2.2
• record linkage 15.3.1
• rectified linear unit 7.5
• recurrent neural network 7.5
• reference point 9.1.3
• reflection 14.4
• regression Task, 7.3.2
• regression planning 6.3, 6.3
• regression to the mean 7.4, 7.4.1
• regression tree 7.6
• regularization Regularize, 7.4.2
• regularization parameter 7.4.2
• regularizer 7.4.2
• regulatory capture 16.2
• reify 14.2.1, 15.1
• reinforcement learning 12.1—12.13, Feedback
• rejection sampling 8.6.3
• relation relations, A.3, 1.5.3, 13.1, 4.1.2
• relational learning 15.2
• relational algebra A.3
• relational database A.3
• relational probability model 15.3.1
• relational representations 1.5.3
• relational uncertainty 15.3.1
• remember 2.2.1, 2.3
• representation 1.4.2
• representation bias 7.4
• representation dimension 1.5.3, 1.5.3
• representation language 1.4.2
• resampling 8.6.6
• resolution 5.3.2, 5.3.2
  • SLD 13.4.4, 5.3.2
• resolvent 5.3.2
• resource 14.3
• Resource Description Framework 14.3
• resource goal 6.1.4
• restriction bias 7.8.1
• retry 3.4
• return 12.4, 9.5
• revelation principle 11.6
• reward 9.5
• reward function 9.5
• rewrite rule 13.6.1, 13.7.1
• ridge regression 7.4.2
• risk averse 9.1.1
• RL, see reinforcement learning
• RoboCup 16.2
• robot 1.3, 1.6.1, 2.1
• ROC space 7.2.2
• role 17, 14.3.3
• root 3.3.1
• root-mean-square (RMS) error 7.2.1
• rule 13.3, 13.3, 5.3
• rule of inference 5.3.2
• run 11.2.2
• run-time distribution 4.7.4
• running average 12.3, 7.4.1
• safety 1.4.1, 16.2, 16.2
• safety goal 6.1.4
• sample average 8.6.1
• sample complexity 10.4, 7.8.2
• SARSA 12.7
  • algorithm Figure 12.5
• SARSA with linear function approximation 12.9.1
  • algorithm Figure 12.7
• satisfiable 5.7
• satisficing solution Satisficing solution, 3.1
• satisfy
  • constraint 4.1.2, 4.1.2
  • policy 9.3.2
• scenario 5.7
• scheme A.3
• scientific goal 1.1
• scope A.2, A.3, 4.1.2, 4.9, 8.4.2
• search Chapter 3—3.11
  • $A^{*}$ 3.6.1
  • algorithm Figure 3.4
  • bidirectional 3.8.2
  • breadth-first 3.5.1
  • cycle-pruning 3.7.1
  • depth-first 3.5.2
  • domain splitting 4.5
  • dynamic programming 3.8.3
  • for CSPs 4.3
  • gradient descent 4.9.2
  • greedy best-first 3.6
  • heuristic 3.6
  • island driven 3.8.2
  • iterative deepening 3.5.3
  • local search 4.7
  • lowest-cost-first 3.5.4
  • minimax 11.3
  • policy 12.2
  • stochastic local 4.7.2
• search and score 10.3.4
• search bias 7.4, 7.8.1
• search strategy 3.5
• second-order logic 13.5
• second-price auction 11.6
• select 3.4, 5.3.2
• selection A.3
• selector function 15.3.1
• semantic interoperability 14.1, 2.4.4
• semantic network 14.2.2
• semantic web 14.3
• semantics Figure 13.1, 5.1.2
  • Datalog 13.3.1
  • natural language Semantics
  • propositional calculus 5.1.2
  • variables 13.3.2
• semi-decidable logic 13.5
• sensing uncertainty dimension 1.5.6
• sensor 2.4.4, 2.1, 2.2
• sensor fusion Example 8.32
• sentence 13.6.1, 8.5.6
• separable control problem 2.4.1
• sequential Monte Carlo 8.6.6
• sequential decision problem 9.3
• sequential prisoner’s dilemma 11.4
• set sets
• set difference A.3
• set-of-words 8.5.6
• short-term memory 2.4.2
• sigmoid function 7.5, 7.3.2, 8.4.2
• sigmoid layer (in neural network)
  • class Figure 7.17
• simulated annealing 4.7.3
• simulated annealing 4.7.3
• simultaneous action games 11.2.2
• single agent 1.5.8
• single decision 9.2
• single-stage decision network 9.2.1
• singularity 16.2
• situation 15.1.1
• situation calculus 15.1.1
• SLD resolution 5.3.2
• SLD derivation 13.4.4, 5.3.2
• SLD resolution 13.4.4
• Smalltalk 14.2.3
• smart house 1.6, 1.6.5
• smoothing 8.5.2, 8.5.3
• SNLP 14.
• social preference function 11.5
• society 1.1.1
• soft constraint 4.9
• soft clustering 10.2
• soft constraint Chapter 4
• soft-max 12.5
• software agent 1.3
• software engineering 1.4.3
• solution 3.2, 3.3.1
• sound 5.3.2
• spatio-temporal region 30, 14.3.3
• specialization operator 15.2.1
• specific boundary 7.8.1
• specific-to-general search 15.2.1
• squashed linear function 7.3.2
• stable assignment 10.2.1
• stack 3.5.2
• stage 1.5.2, 8.5.1
• start node 3.3.1
• start state 3.2
• starvation 13.5.1, 3.4
• state 1.5.3, 3.2, 8.5.1
• state constraint 6.4
• state features 6.4.1
• state space 3.2
• state variable 6.4
• state-space graph 6.2
• state-space graph 6.1.1
• state-space problem 3.2
• static relation 15.1.1, 15.1.1
• stationary distribution 8.6.7
• stationary distribution 8.5.1
• stationary model 8.5.4, 8.5.1, 9.5
• stationary policy 9.5.1
• statistical relational AI 15.3—15.3.1
• step size 4.9.2
• step function 7.3.2
• step size 4.9.2
• stimuli 1.3, 2.2
• stochastic beam search 4.8
• stochastic dynamics 1.5.6
• stochastic gradient descent 7.3.2
• stochastic gradient descent 7.5
• stochastic local search 4.7.2
• stochastic policy 12.10.2, 12.10.2
• stochastic simulation 8.6
• stochastic strategy 11.4
• stopping state 9.5
• strategic agent 11.1
• strategic form game 11.2.1
• strategy 11.2.1, 11.2.2, 11.4
• strategy profile 11.2.2, 11.4
• strictly dominated 11.4.1, 4.9.1
• strictly preferred 9.1.1
• STRIPS assumption 6.1.2
• STRIPS representation 6.1.2, 6.1.2
• structure learning 10.3.4
• subgame-perfect equilibrium 11.4
• subgoal 5.3.2, 5.3.2, 6.3
• subject 13.6.6, 14.2.1
• subjective probability 8.1
• substitutes 9.1.2
• substitution 13.4.1
• successor 4.7
• sufficient statistics 10.2.2
• sum-of-squares error 10.2.1, 7.2.1, 7.3.2, 7.3.2
• supervised learning 10.3.1, Feedback
• supervised learning Task, 7.11, 7.2—7.2
• support set 2., 11.4
• support vector machine (SVM) 7.6
• sustainable system 16.2
• SVM, see support vector machine
• symbol 1.4.4, 4.1.1
• symbol level 1.4.4
• symbol system 1.4.4
• symptoms 1.6.2
• syntax
  • Datalog 13.3
  • natural language Syntax
  • propositional definite clauses 5.3
• synthesis 1.1
• systematicity 14.
• systems 1 and 2 2.6
• systems 1 and 2 2.3
• tabu search 4.7.3
• tabu tenure 4.7.3
• target features 7.2
• TBox 14.3.2
• TD error 12.3
• tell 2.3, Step 3
• temperature 12.5
• temporal difference error 12.3
• temporal region 32, 14.3.3
• term 13.3, 13.5
• terminal symbol 13.6.1
• terminological knowledge base 14.3.2
• test examples Measuring success, 7.2
• theorem 5.3.2
• there exists ($\mathrm{\exists }$) 13.3.2
• thing 1.5.3, 13.1, 14.3.3
• thought 1.1
• threat Example 11.14
• time 15.1, 2.2.1
• time granularity 8.5.5
• time-homogenous model 8.5.1
• tit-for-tat 11.4
• TMS, see truth maintenance system
• top-down proof procedure 5.3.2
• top-level ontology 14.3.3
• top-n 15.2.2
• topic model 8.5.6
• total assignment 4.1.1
• total reward Total reward
• tractable 1.4.2
• trading agent 1.6, 1.6.4
• training examples 7.8, Task, Measuring success, 7.2
• transduction 2.2.1
• transient goal 6.1.4
• transitivity of preferences Axiom 9.2
• tree 3.3.1
• tree augmented naive Bayes (TAN) network 10.1.2
• treewidth 4.6, 8.4.1
• triangle inequality 3.7.2
• trigram 8.5.6
• triple tuples, 14.2.1, Example 15.12
• triple representation 14.2.1
• trolley problems 9.1.3
• true relations, 13.3.1, 5.1.2
• true-positive rate 7.2.2
• trust 16.2, 16.2
• truth maintenance system 5.10
• truthful 11.6
• try (local search) 4.7
• tuple tuples, A.3
• Turing test 1.1.1
• tutoring system 1.6
• two-stage choice 4.7.3
• two-step belief network 8.5.4
• $\mathrm{t}\mathrm{y}\mathrm{p}\mathrm{e}$ Example 14.5
• type I error 7.2.2
• type II error 7.2.2
• UML 14.2.3
• unary constraint 4.1.2
• unary relations 13.1
• unbiased learning algorithm 7.8.1
• unconditionally independent 8.2
• unfolded network 8.5.4, 9.5.4
• unification 13.4.3, 13.4.3
  • algorithm Figure 13.3
• unifier 13.4.1
• Uniform Resource Identifier 14.3, 14.3.1
• unigram 8.5.6
• uninformed search strategy 3.5, 3.6
• union A.3
• unique names assumption (UNA) 13.7.2
• unit (neural network) 7.5
• unit resolution 5.2.1
• universal basic income 16.2
• universally quantified variable 13.3.2, 13.3.2
• unnormalized probabilities 8.4.1
• unsatisfiable 5.5.1
• unsupervised learning 10.2, 10.2.2, Feedback
• URI 14.3, 14.3.1
• useful action 6.3
• user 2.4.4, 4.1.1, 5.4.1
• utility Optimal solution, Proposition 9.3
• utility node 9.2.1
• $V^{\pi }$ 9.5.1, 9.5.1
• $V^{*}$ 9.5.1
• validation set 7.4.3
• value 9.5
  • decision network 9.2.1
• value iteration 9.5.2
  • algorithm Figure 9.16
• value of control 9.4
• value of information 9.4
• vanilla meta-interpreter Figure 14.9, 14.4.2
• variable Chapter 4, 4.1.1
  • action 6.4
  • algebraic 4.1.1
  • Boolean 4.1.1, 5.2
  • continuous 4.1.1
  • decision 9.2
  • discrete 4.1.1
  • existentially quantified 13.3.2
  • logical 13.3
  • random 8.1.1
  • state 6.4
  • universally quantified 13.3.2, 13.3.2
• variable elimination 4.6
• variable assignment 13.3.2
• variable elimination 8.4.1
  • belief network 8.4.1
    • algorithm Figure 8.10
  • CSP 4.6
    • algorithm Figure 4.6
  • decision network 9.3.3
    • algorithm Figure 9.13
  • monitoring 8.5.3
  • single-stage decision network 9.2.1
    • algorithm Figure 9.8
  • soft constraints 4.9.1
  • soft constraints
    • algorithm Figure 4.12
• variance 7.4
• variational inference Approximate inference
• VCG mechanism 11.6
• VE, see variable elimination
• verb 13.6.6, 14.2.1
• version space 7.8.1
• violated 4.1.2
• virtual body 2.3
• walk 4.7
• Watson 1.2, 13.10
• weak learner 7.6.2
• weakly dominated 4.9.1
• weakly preferred 9.1.1
• Web Ontology Language 14.3
• web services 1.6.4
• why question 5.4.3
• whynot question 5.4.3, 5.4.4
• Wikidata 14.6
• win or learn fast (WoLF) 12.10.2
• Winograd schema 1.1.1
• word 13.3, 13.6.1, 8.5.6
• world 1.3
  • possible 15.3.1, 4.1.1, 8.1.1, 9.2
• World Wide Mind 16.2
• worst-case error 7.2.1
• wrapper 2.4.4
• XML 14.3
• YAGO 14.6
• yes answer 5.3.1
• Zeno’s paradox 3.5.4
• zero-sum game 11.1, 11.3
• $ϵ$-greedy exploration strategy 12.5
• $\pi$ (denotation of predicate symbols) 13.3.1
• $\pi$ (meaning of atoms) 5.1.2
• $\varphi$ (denotation of terms) 13.5
• $\varphi$ (denotation of terms) 13.3.1