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This is a survey of major AI textbooks and a few academic course listings, designed to determine for Wikipedia what topics are essential to an introduction to artificial intelligence. This is intended to help the central articles about artificial intelligence to pass the featured article criteria. It should be noted that there is a great deal of consensus among experts on what subjects constitute the whole field of AI research.

Textbooks

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These are listed on the list of textbooks at AI Topics, which also lists their relative popularity. These are the four most popular textbooks published since 1998 (i.e. in the ten years before this survey was done.)

Russell & Norvig (standard AI textbook)

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Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2 Chapters:

  • 1 Introduction History of AI, some philosophy of AI
  • 2 Intelligent agent paradigm
  • 3-6 Search
  • 7-9 Logic
  • 10 Knowledge representation
  • 11-12 Planning
  • 13-17 Uncertain reasoning
  • 18-21 Learning
  • 22-23 Natural language processing (they call "communication")
  • 24 Perception
  • 25 Robotics
  • 26 Philosophy of AI
  • 27 future of AI

Nilsson

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Nilsson, Nils (1998), Artificial Intelligence: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-55860-467-4

1 Introduction

I Reactive Machines

2 Stimulus-Response Agents
3 Neural Networks
4 Machine Evolution
5 State Machines
6 Robot Vision

II Search in State Spaces

7-9 search, uninformed, heuristic
10 Planning, Acting, and Learning chapter is actually mostly about search, I think...
11 Alternative Search Formulations and Applications
12 Adversarial Search

III Knowledge Representation and Reasoning

13-16 The Propositional, Predicate Calculus and resolution
17 Knowledge-Based Systems
18 Representing Commonsense Knowledge
19 Reasoning with Uncertain Information
20 Learning and Acting with Bayes Nets

IV Planning Method Based on Logic

21 The Situation Calculus
22 Planning

V Communication and Integration

23 Multiple Agents
24 Communication Among Agents Natural Language Processing
25 Agent Architectures

Luger & Stubblefield

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  • Luger, George; Stubblefield, William (2004), Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th ed.), The Benjamin/Cummings Publishing Company, Inc., p. 720, ISBN 0-8053-4780-1
  • 1 ARTIFICIAL INTELLIGENCE: ITS ROOTS AND SCOPE 1
  • 2 THE PREDICATE CALCULUS 45
  • 3-4 STRUCTURES AND STRATEGIES FOR STATE SPACE SEARCH 79 (including Hill Climbing and Dynamic Programming)
  • 5 STOCHASTIC METHODS 165
  • 6 CONTROL AND IMPLEMENTATION OF STATE SPACE SEARCH 193
  • 7 KNOWLEDGE REPRESENTATION 227
  • 8 STRONG METHOD PROBLEM SOLVING (Expert systems) 277
  • 9 REASONING IN UNCERTAIN SITUATIONS 333
  • 10-12 MACHINE LEARNING: SYMBOL-BASED 387 / CONNECTIONIST 453 / SOCIAL AND EMERGENT 507 (including: Genetic, classifier, artificial life)
  • 13 AUTOMATED REASONING 547
  • 14 UNDERSTANDING NATURAL LANGUAGE 591
  • 15 PROLOG 636
  • 16 AN INTRODUCTION TO LISP 723
  • 17 ARTIFICIAL INTELLIGENCE AS EMPIRICAL ENQUIRY 823

Poole & Macworth

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Poole, David; Mackworth, Alan; Goebel, Randy (1998), Computational Intelligence: A Logical Approach, Oxford University Press {{citation}}: Unknown parameter |publisher-place= ignored (help)

  • Chapter 1 Computational Intelligence and Knowledge introduction
  • Chapter 2 A Representation and Reasoning System forward and backward chaining
  • Chapter 3 Using Definite Knowledge includes databases and natural language
  • Chapter 4 Searching includes standard state space searches, dynamic programming, constraint satisfation, hill climbing, "randomization algortihms" and genetic algorithms
  • Chapter 5 Representing Knowledge
  • Chapter 6 Knowledge Engineering,
  • Chapter 7 Beyond Definite Knowledge includes first order logic, proof systems
  • Chapter 8 Actions and Planning
  • Chapter 9 Assumption-Based Reasoning, default reasoning, abduction
  • Chapter 10 Using Uncertain Knowledge
  • Chapter 11 Learning
  • Chapter 12 Building Situated Robots

Other textbooks

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Rich & Knight

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Rich, Elaine (1991), Artificial Intelligence (2nd ed.), New York: McGraw-Hill, ISBN 0-07-052263-4 {{citation}}: |first2= missing |last2= (help); Unknown parameter |laast2= ignored (help)

  • What is Artificial Intelligence?
  • Problems, Problem Spaces, and Search. Heuristic Search Techniques.
  • Knowledge Representation. Knowledge Representation Issues.
  • Using Predicate Logic. Representing Knowledge Using Rules.
  • Symbolic Reasoning Under Uncertainty. Statistical Reasoning.
  • Weak Slot-and-Filler Structures. Strong Slot-and-Filler Structures. Knowledge Representation Summary.
  • Game Playing.
  • Planning.
  • Understanding.
  • Natural Language Processing.
  • Parallel and Distributed AI.
  • Learning.
  • Connectionist Models.
  • Common Sense.
  • Expert Systems.
  • Perception and Action.
  • Conclusion.

Cawsey

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Cawsey, Alison (1998), Essence of Artificial Intelligence, Prentice Hall, ISBN 0135717795

  • Introduction.
  • Knowledge Representation and Inference.
  • Expert Systems.
  • Using Search in Problem Solving.
  • Natural Language Processing.
  • Vision.
  • Machine Learning and Neural Networks.
  • Agents and Robots.

Murray

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Murray, Arthur (2002), AI4U, iUniverse, ISBN 0595259227

  • Introduction.
  • 1-34 Modules of the AI Mind; Exercises.
  • JavaScript source code of the tutorial AI Mind.

Poole and Mackworth (2010)

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Artificial Intelligence: Foundations of Computational Agents

I Agents in the World: What Are Agents and How Can They Be Built?
1 Artificial Intelligence and Agents
2 Agent Architectures and Hierarchical Control
II Representing and Reasoning
3 States and Searching
4 Features and Constraints
5 Propositions and Inference
6 Reasoning Under Uncertainty
III Learning and Planning
7 Learning: Overview and Supervised Learning
8 Planning with Certainty
9 Planning Under Uncertainty
10 Multiagent Systems
11 Beyond Supervised Learning
IV Reasoning About Individuals and Relations
12 Individuals and Relations
13 Ontologies and Knowledge-Based Systems
14 Relational Planning, Learning, and Probabilistic Reasoning
V The Big Picture
15 Retrospect and Prospect

Cambridge Handbook of Artificial Intelligence (2014)

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Part I: Foundations
1. History, motivations, and core themes
2. Philosophical foundations
3. Philosophical challenges
Part II: Architectures
4. GOFAI
5. Connectionism and neural networks
6. Dynamical systems and embedded cognition
Part III: Dimensions
7. Learning
8. Perception and computer vision
9. Reasoning and decision making
10. Language and communication
11. Actions and agents
12. Artificial emotions and machine consciousness
Part IV: Extensions
13. Robotics
14. Artificial life
15. The ethics of artificial intelligence

ACM classification

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ACM, (Association of Computing Machinery) (1998), ACM Computing Classification System: Artificial intelligence

  • I.2.0 General
  • I.2.1 Applications and Expert Systems (H.4, J) considered in this in the section "Applications"
  • I.2.2 Automatic Programming (D.1.2, F.3.1, F.4.1) not considered AI by wikipedia
  • I.2.3 Deduction and Theorem Proving (F.4.1)
  • I.2.4 Knowledge Representation Formalisms and Methods (F.4.1)
  • I.2.5 Programming Languages and Software (D.3.2)
  • I.2.6 Learning (K.3.2)
  • I.2.7 Natural Language Processing
  • I.2.8 Problem Solving, Control Methods, and Search (F.2.2) control theory, dynamic programming, search, planning & scheduling
  • I.2.9 Robotics
  • I.2.10 Vision and Scene Understanding (I.4.8, I.5)
  • I.2.11 Distributed Artificial Intelligence

Websites

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Sloman

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Sloman, Aaron (2007), Artificial intelligence: an illustrative overview, University of Birmingham

  • Perception
  • Natural language processing
  • Learning
  • Planning, problem solving, automatic design
  • Varieties of reasoning
  • Study of representations (knowledge representation)
  • Memory mechanisms and techniques
  • Multi agent systems
  • Affective mechanisms
  • Robotics
  • Architectures for complete systems.
  • Search
  • Ontologies

Leake

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Leake, David B. (2002), "Artificial intelligence", Van Nostrand Scientific Encyclopedia (ninth ed.), New York: Wiley

  • Knowledge capture, representation and reasoning
  • Reasoning under uncertainty
  • Planning, Vision, and Robotics
  • Natural language processing
  • Machine Learning


Bringing it all together

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This table lists (just about) every topic that appears in the title of a section or in a chapter summary of Russell & Norvig (2003), the most popular AI textbook. Information for the other textbooks is based on their tables of contents, available online. Several topics appear more than once, in different contexts.

Subject ACM 1998 Russell & Norvig 2003 Poole, Mackworth & Goebel 1998 Luger & Stubblefield 2004 Nilsson 1998
Defining AI and philosophy of AI[1] I.2.0 pp. 1-5, 947-967 pp. 1-6 pp. 1-2, 30, ~823-848[2] ~chpt. 1.1[2]
History of AI[3] pp. 5-28 pp. 3-30 chpt. 1.3
Approaches to AI[4] chpt. 1.2
Future of AI[5] pp. 968-974 pp. 848-853
Intelligent agent paradigm[6] pp. 32-58, 968-972 pp. 7-21 pp. 235-240
Agent architecture)[7] I.2.11 pp. 27, 932, 970-972 chpt. 25
Search[8] ~I.2.8[2] pp. 59-189 pp. 113-163 pp. 79-164, 193-219 chpt. 7-12
Standard searches (breadth first, depth first, backtracking, state space, graph, etc.)[9] pp. 59-93 pp. 113-132 pp. 79-121 chpt. 8
Informed Heuristic searches (greedy best first, A*, dynamic programming, etc.)[10] pp. 94-109 pp. 132-147 pp. 133-150 chpt. 9
Local search and optimization searches (hill climbing, simulated annealing, beam search, continuous search (i.e. Hessian matrix searches)), exploratory search ("online search" and random walk searches)[11] pp. 110-116,120-129 pp. 56-163[12] pp. ~127-133[2]
Genetic algorithms[13] pp. 116-119 pp. 162 pp. 509-530 chpt. 4.2
Constraint satisfaction[14] pp. 137-156 pp. 147-163
Adversarial search (minimax, alpha-beta pruning, using utility)[15] pp. 161-185 pp. 150-157 chpt. 12
Logic[16] ~I.2.3[2] pp. 194-310 various pp. 35-77 chpt. 13-16
Propositional logic[17] pp. 204-233 various pp. 45-50 chpt. 13
First order logic (incl. equality)[18] ~I.2.4[2] pp. 240-310 pp. 268-275 pp. 50-62 chpt. 15
Inference (and inference engine, production system, logic programming)[19] pp. 213-224, 272-310 pp. 46-58 pp. 62-73, 194-219, 547-589 chpt. 14 & 16
Resolution and unification[20] pp. 213-217, 275-280, 295-306 pp. 56-58 pp. 554-575 chpt. 14 & 16
Forward and backward chaining (also Horn clause): a form of search[21] pp. 217-225, 280-294 pp. ~46-52[2] ~chpt. 17.2[2]
Theorem provers[22] pp. 306-310
Truth maintenance systems[23] pp. 360-362
Knowledge representation[24] I.2.4 pp. 320-363 pp. 23-46, 69-81, 169-196, 235-277, 281-298, 319-345 pp. 227-243 chpt. 18
Ontology[25] pp. 320-328
Representing events and time: Situation calculus, event calculus, fluent calculus (including solving the frame problem)[26] pp. 328-341 pp. 281-298 chpt. 18.2
Representing knowledge about knowlege: Belief calculus, modal logics[27] pp. 341-344 pp. 275-277
Representing categories and relations: Semantic networks, description logics, inheritance, (including the deprecated[28] concept of frames and scripts)[29] pp. 349-354 pp. 174-177 pp. 248-258 chpt. 18.3
Default reasoning and default logic, non-monotonic logics, circumscription, closed world assumption, abduction[30][31] pp. 354-360 pp. 248-256, 323-335 pp. 335-363 ~chpt. 18.3.3[2]
Causal calculus[32] pp. 335-337
Knowledge engineering[33] pp. 260-266 pp. 199-233 ~chpt. 17.1-17.4[2]
Knowledge acquisition: getting information from experts.[34] pp. 260 pp. 212-217
Explanation[35] pp. 217-220
Planning[36] ~I.2.8[2] pp. 375-459 pp. 281-316 pp. 314-329 chpt. 10 & 21
State space search and planning[37] pp. 382-387 pp. 298-305 chpt. 10
Partial order planning[38] pp. 387-395 pp. 309-315
Graph planning[39] pp. 395-402
Planning with propositional logic (satplan)[40] pp. 402-407 pp. 300-301 chpt. 21
Hierarchical task network[41] pp. 422-430
Planning and acting in non-deterministic domains, conditional planning; search in the space of belief states, execution monitoring, replanning and continuous planning.[42] pp. 430-449
Multi-agent planning[43] pp. 449-455
Stochastic tools and uncertain reasoning.[44] ~I.2.3[2] pp. 462-644 pp. 345-395 pp. 165-191, 333-381 chpt. 19
Probability[45] pp. 462-489 pp. 346-366 pp. ~165-182[2] chpt. 19.1
Bayesian networks[46] pp. 492-523 pp. 361-381 pp. ~182-190, ~363-379[2] chpt. 19.3-4, 19.7
Bayesian inference[47] pp. 504-519 pp. 361-381 pp. ~363-379[2] chpt. 19.4
Polytrees[48] chpt. 19.7
Deprecated methods for uncertain reasoning[28][49] pp. 523-528
Certainty factors[50] pp. 524-525
Dempster-Shafer theory: measuring ignorance[51] pp. 525-526
Fuzzy logic: degrees of truth[52] pp. 526-527
Temporal models (Markov property) used for filtering, prediction, smoothing and computing the most likely explanation[53] pp. 537-581
Hidden Markov models[54] pp. 549-551
Kalman filters[55] pp. 551-557
Dynamic Bayesian networks[56] pp. 551-557
Decision theory or decision analysis (= utility theory + probability theory)[57] pp. 584-604 pp. 381-394
Bayesian Decision networks[58] pp. 597-600
Information value theory[59] pp. 600-604
Markov decision processes, and dynamic decision networks[60] pp. 613-631
Game theory and its "inverse", mechanism design[61] pp. 631-643
Learning (supervised (inductive) / unsupervised / reinforcement)[62] I.2.6 pp. 649-788 pp. 397-438 pp. 385-542 chpt. 3.3 , 10.3, 17.5, 20
Symbolic[63] pp. 653-736, 763-788 pp. 387-450
Decision tree[64] pp. 653-664 pp. 403-408 pp. 408-417
Explanation based learning, relevance based learning, inductive logic programming, case based reasoning[65] pp. 678-710 pp. 414-416 pp. ~422-442[2] chpt. 10.3, 17.5
Statistical[66] pp. 712-754 pp. 453-541
Reinforcement learning (uses elements of decision theory, like utility)[67] pp. 763-788 pp. 442-449[68]
Bayesian learning, including expectation-maximization algorithm[69] pp. 712-724 pp. 424-433 chpt. 20
K-nearest neighbor algorithm[70] pp. 733-736
kernel methods[71] pp. 749-752
Connectionism and neural nets[72] pp. 736-748 pp. 408-414 pp. 453-505 chpt. 3
Perceptron[73] pp. 740-743 pp. 458-467
Backpropagation[74] pp. 744-748 pp. 467-474 chpt. 3.3
Competitive learning, Hebbian coincidence learning, Attractor networks[75] pp. 474-505
Social and emergent[76] pp. 507-542 chpt. 4
Classifiers and genetic algorithms[77] pp. 509-530 chpt. 4.2
Artificial life and society based learning[78] pp. 530-541
Natural language processing[79] I.2.7 pp. 790-831 pp. 91-104 pp. 591-632
Syntax and parsing[80] pp. 795-810 pp. 597-616
Semantics and disambiguation[81] pp. 810-821
Discourse understanding: coherence relations, speech acts, pragmatics[82] pp. 820-824
Probabilistic methods (learning)[83] pp. 834-840 pp. 616-623
Applications[84] pp. 840-857 pp. 623-630
Information retrieval and text mining[85] pp. 840-850
Machine translation[86] pp. 850-857
Perception[87] pp. 537-581, 863-898 ~chpt. 6[2]
Perception with stochastic temporal models[88] pp. 547-581
Hidden markov models[89] pp. 549-551
Kalman filters[90] pp. 551-559
Dynamic Bayesian networks[91] pp. 559-568
Speech recognition[92] ~I.2.7[2] pp. 568-578
Machine vision[93] I.2.10 pp. 863-898 chpt. 6
Robotics[94] I.2.9 pp. 901-942 pp. 443-460
Control theory[95] ~I.2.8[2] pp. 926-932
Specialized languages[96] I.2.5 pp. 477-491 pp. 641-821
Prolog[97] pp. 477-491 pp. 641-676, 575-581
Lisp[98] p. 723-821
Applications of AI[99] I.2.1
Expert systems[100] I.2.1 (several mentions) pp. 227-331 chpt. 17.4
Automatic programming (other sources don't consider this AI)[101] I.2.2

Notes

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  1. ^ ACM 1998, I.2.0, Russell & Norvig 2003, pp. 1–5, 947–967, Poole, Mackworth & Goebel 1998, pp. 1–6, Luger & Stubblefield 2004, pp. 1–2, 30, ~823-848, Nilsson 1998, ~chpt. 1.1
  2. ^ a b c d e f g h i j k l m n o p q r s Contained in this section, but not referred to in the title of the section by name.
  3. ^ Russell & Norvig 2003, pp. 5–28, Luger & Stubblefield 2004, pp. 3–30, Nilsson 1998, chpt. 1.3
  4. ^ Nilsson 1998, chpt. 1.2
  5. ^ Russell & Norvig 2003, pp. 968–974, Luger & Stubblefield 2004, pp. 848–853
  6. ^ Russell & Norvig 2003, pp. 32–58, 968–972, Poole, Mackworth & Goebel 1998, pp. 7–21, Luger & Stubblefield 2004, pp. 235–240
  7. ^ ACM 1998, I.2.11, Nilsson 1998, chpt. 25
  8. ^ ACM 1998, ~I.2.8, Russell & Norvig 2003, pp. 59–189, Poole, Mackworth & Goebel 1998, pp. 113–163, Luger & Stubblefield 2004, pp. pp. 79-164, 193–219, Nilsson 1998, chpt. 7-12
  9. ^ Russell & Norvig 2003, pp. 59–93, Poole, Mackworth & Goebel 1998, pp. 113–132, Luger & Stubblefield 2004, pp. 79–121, Nilsson 1998, chpt. 8
  10. ^ Russell & Norvig 2003, pp. 94–109, Poole, Mackworth & Goebel 1998, pp. 132–147, Luger & Stubblefield 2004, pp. 133–150, Nilsson 1998, chpt. 9
  11. ^ Russell & Norvig 2003, pp. 110–116, 120–129, Poole, Mackworth & Goebel 1998, pp. 56–163, Luger & Stubblefield 2004, pp. ~127-133
  12. ^ Poole discusses local searches under the topic of constraint satisfaction.
  13. ^ Russell & Norvig 2003, pp. pp. 116-119, Poole, Mackworth & Goebel 1998, p. 162, Luger & Stubblefield 2004, pp. 509–530, Nilsson 1998, chpt. 4.2
  14. ^ Russell & Norvig 2003, pp. 137–156, Poole, Mackworth & Goebel 1998, pp. 147–163
  15. ^ Russell & Norvig 2003, pp. 161–185, Luger & Stubblefield 2004, pp. 150–157 chpt. 12
  16. ^ ACM 1998, ~I.2.3, Russell & Norvig 2003, pp. 194–310, Luger & Stubblefield 2004, pp. 35–77, Nilsson 1998, chpt. 13-16
  17. ^ Russell & Norvig 2003, pp. 204–233, Luger & Stubblefield 2004, pp. 45–50 Nilsson 1998, chpt. 13
  18. ^ ACM 1998, ~I.2.4, Russell & Norvig 2003, pp. 240–310, Poole, Mackworth & Goebel 1998, pp. 268–275, Luger & Stubblefield 2004, pp. 50–62, Nilsson 1998, chpt. 15
  19. ^ Russell & Norvig 2003, pp. 213–224, 272–310, Poole, Mackworth & Goebel 1998, pp. 46–58, Luger & Stubblefield 2004, pp. 62–73, 194–219, 547–589, Nilsson 1998, chpt. 14 & 16
  20. ^ Russell & Norvig 2003, pp. 213–217, 275–280, 295–306, Poole, Mackworth & Goebel 1998, pp. 56–58, Luger & Stubblefield 2004, pp. 554–575, Nilsson 1998, chpt. 14 & 16
  21. ^ Russell & Norvig 2003, pp. 217–225, 280–294, Poole, Mackworth & Goebel 1998, pp. ~46-52, Nilsson 1998, ~chpt. 17.2
  22. ^ Russell & Norvig 2003, pp. 306–310
  23. ^ Russell & Norvig 2003, pp. 360–362
  24. ^ ACM 1998, I.2.4, Russell & Norvig 2003, pp. 320–363, Poole, Mackworth & Goebel 1998, pp. 23–46, 69–81, 169–196, 235–277, 281–298, 319–345 Luger & Stubblefield 2004, pp. 227–243, Nilsson 1998, chpt. 18
  25. ^ Russell & Norvig 2003, pp. 320–328
  26. ^ Russell & Norvig 2003, pp. 328–341, Poole, Mackworth & Goebel 1998, pp. 281–298, Nilsson 1998, chpt. 18.2
  27. ^ Russell & Norvig 2003, pp. 341–344, Poole, Mackworth & Goebel 1998, pp. 275–277
  28. ^ a b According to Russell and Norvig.
  29. ^ Russell & Norvig 2003, pp. 349–354, Poole, Mackworth & Goebel 1998, pp. 174–177, Luger & Stubblefield 2004, pp. 248–258, Nilsson 1998, chpt. 18.3
  30. ^ Poole et al. places abduction under "default reasoning". Luger et. al. places this under "uncertain reasoning"
  31. ^ Russell & Norvig 2003, pp. 354–360, Poole, Mackworth & Goebel 1998, pp. 248–256, 323–335 Luger & Stubblefield 2004, pp. 335–363, Nilsson 1998, ~18.3.3
  32. ^ Poole, Mackworth & Goebel 1998, pp. 335–337
  33. ^ Russell & Norvig 2003, pp. 260–266, Poole, Macworth & Goebel 1998, pp. 199–233, Nilsson 1998, chpt. ~17.1-17.4
  34. ^ Russell & Norvig 2003, p. 260, Poole, Mackworth & Goebel 1998, pp. 212–217
  35. ^ Poole, Mackworth & Goebel 1998, pp. 217–220
  36. ^ ACM 1998, ~I.2.8, Russell & Norvig 2003, pp. 375–459, Poole, Mackworth & Goebel 1998, pp. 281–316, Luger & Stubblefield 2004, pp. 314–329, Nilsson 1998, chpt. 10 & 21
  37. ^ Russell & Norvig 2003, pp. 382–387, Poole, Mackworth & Goebel 1998, pp. 298–305, Nilsson 1998, chpt. 10
  38. ^ Russell & Norvig 2003, pp. 387–395, Poole, Mackworth & Goebel 1998, pp. 309–315
  39. ^ Russell & Norvig 2003, pp. 395–402
  40. ^ Russell & Norvig 2003, pp. 402–407, Poole, Mackworth & Goebel 1998, pp. 300–301, Nilsson 1998, chpt. 21
  41. ^ Russell & Norvig 2003, pp. 422–430
  42. ^ Russell & Norvig 2003, pp. 430–449
  43. ^ Russell & Norvig 2003, pp. 449–455
  44. ^ ACM 1998, ~I.2.3, Russell & Norvig 2003, pp. 462–644, Poole, Mackworth & Goebel 1998, pp. 345–395, Luger & Stubblefield 2004, pp. 165–191, 333–381, Nilsson 1998, chpt. 19
  45. ^ Russell & Norvig 2003, pp. 462–489, Poole, Mackworth & Goebel 1998, pp. 346–366, Luger & Stubblefield 2004, pp. ~165-182, Nilsson 1998, chpt. 19.1
  46. ^ Russell & Norvig 2003, pp. 492–523, Poole, Mackworth & Goebel 1998, pp. 361–381, Luger & Stubblefield 2004, pp. ~182-190, ~363-379, Nilsson 1998, chpt. 19.3-4
  47. ^ Russell & Norvig 2003, pp. 504–519, Poole, Mackworth & Goebel 1998, pp. 361–381, Luger & Stubblefield 2004, pp. ~363-379, Nilsson 1998, chpt. 19.4
  48. ^ Nilsson 1998, chpt. 19.7
  49. ^ Russell & Norvig 2003, pp. 523–528
  50. ^ Russell & Norvig 2003, pp. 524–525
  51. ^ Russell & Norvig 2003, pp. 525–526
  52. ^ Russell & Norvig 2003, pp. 526–527
  53. ^ Russell & Norvig 2003, pp. 537–581
  54. ^ Russell & Norvig 2003, pp. 549–551
  55. ^ Russell & Norvig 2003, pp. 551–557
  56. ^ Russell & Norvig 2003, pp. 551–557
  57. ^ Russell & Norvig 2003, pp. 584–644, Poole, Mackworth & Goebel 1998, pp. 381–394
  58. ^ Russell & Norvig 2003, pp. 597–600
  59. ^ Russell & Norvig 2003, pp. 600–604
  60. ^ Russell & Norvig 2003, pp. 613–631
  61. ^ Russell & Norvig 2003, pp. 631–643
  62. ^ ACM 1998, I.2.6, Russell & Norvig 2003, pp. 649–788, Poole, Mackworth & Goebel 1998, pp. 397–438, Luger & Stubblefield 2004, pp. 385–542 Nilsson 1998, chpt. 3.3 , 10.3, 17.5, 20
  63. ^ Russell & Norvig 2003, pp. 653–736, 763–788, Luger & Stubblefield 2004, pp. 387–450
  64. ^ Russell & Norvig 2003, pp. 653–664, Poole, Mackworth & Goebel 1998, pp. 403–408, Luger & Stubblefield 2004, pp. 408–417
  65. ^ Russell & Norvig 2003, pp. 678–710, Poole, Mackworth & Goebel 1998, pp. 414–416, Luger & Stubblefield 2004, pp. ~422-442, Nilsson 1998, chpt. 10.3, 17.5
  66. ^ Russell & Norvig 2003, pp. 712–754, Luger & Stubblefield 2004, pp. 453–541
  67. ^ Russell & Norvig 2003, pp. 763–788, Luger & Stubblefield 2004, pp. 442–449
  68. ^ Although they consider this "symbolic" learning, Russell and Norvig describe it using statistical concepts. Perhaps it has changed between the two publications.
  69. ^ Russell & Norvig 2003, pp. 712–724, Poole, Mackworth & Goebel 1998, pp. 424–433, Nilsson 1998, chpt. 20
  70. ^ Russell & Norvig 2003, pp. 733–736
  71. ^ Russell & Norvig 2003, pp. 749–752
  72. ^ Russell & Norvig 2003, pp. 736–748, Poole, Mackworth & Goebel 1998, pp. 408–414, Luger & Stubblefield 2004, pp. 453–505, Nilsson 1998, chpt. 3
  73. ^ Russell & Norvig 2003, pp. 740–743, Luger & Stubblefield 2004, pp. 458–467
  74. ^ Russell & Norvig 2003, pp. 744–748, Luger & Stubblefield 2004, pp. 467–474, Nilsson 1998, chpt. 3.3
  75. ^ Luger & Stubblefield 2004, pp. 474–505
  76. ^ Luger & Stubblefield 2004, pp. 507–542, Nilsson 1998, chpt. 4
  77. ^ Luger & Stubblefield 2004, pp. 509–530, Nilsson 1998, chpt. 4.2
  78. ^ Luger & Stubblefield 2004, pp. 530–541
  79. ^ ACM 1998, I.2.7, Russell & Norvig 2003, pp. 790–831, Poole, Mackworth & Goebel 1998, pp. 91–104, Luger & Stubblefield 2004, pp. 591–632
  80. ^ Russell & Norvig 2003, pp. 795–810, Luger & Stubblefield 2004, pp. 597–616
  81. ^ Russell & Norvig 2003, pp. 810–821
  82. ^ Russell & Norvig 2003, pp. 820–824
  83. ^ Russell & Norvig 2003, pp. 834–840, Luger & Stubblefield 2004, pp. 616–623
  84. ^ Russell & Norvig 2003, pp. 840–857, Luger & Stubblefield 2004, pp. 623–630
  85. ^ Russell & Norvig 2003, pp. 857–850
  86. ^ Russell & Norvig 2003, pp. 850–857
  87. ^ Russell & Norvig 2003, pp. 537–581, 863–898, Nilsson 1998, ~chpt. 6
  88. ^ Russell & Norvig 2003, pp. 547–581
  89. ^ Russell & Norvig 2003, pp. 549–551
  90. ^ Russell & Norvig 2003, pp. 551–559
  91. ^ Russell & Norvig 2003, pp. 559–568
  92. ^ ACM 1998, ~I.2.7, Russell & Norvig 2003, pp. 568–578
  93. ^ ACM 1998, I.2.10, Russell & Norvig 2003, pp. 863–898, Nilsson 1998, chpt. 6
  94. ^ ACM 1998, I.2.9, Russell & Norvig 2003, pp. 901–942, Poole, Mackworth & Goebel 1998, pp. 443–460
  95. ^ ACM 1998, ~I.2.8, Russell & Norvig 2003, pp. 926–932
  96. ^ ACM 1998, I.2.5, Poole, Mackworth & Goebel 1998, pp. 477–491, Luger & Stubblefield 2004, pp. 641–821
  97. ^ Poole, Mackworth & Goebel 1998, pp. 477–491, Luger & Stubblefield 2004, pp. 641–676, 575–581
  98. ^ Luger & Stubblefield 2004, pp. 723–821
  99. ^ ACM 1998, I.2.1
  100. ^ ACM 1998, I.2.1, Luger & Stubblefield 2004, pp. 227–331, Nilsson 1998, chpt. 17.4
  101. ^ ACM 1998, I.2.2

CharlesGillingham (talk) 00:05, 30 November 2007 (UTC)[reply]

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