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Presents a guide to artificial intelligence, covering such topics as intelligent agents, problem-solving, logical agents, planning, uncertainty, learning, and robotics.

The long-anticipated revision of this best-selling book offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First-order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural Networks. Reinforcement Learning. Knowledge in Learning. Agents that Communicate. Practical Communication in English. Perception. Robotics. For those interested in artificial intelligence.

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CONTENTS
Ⅰ Artificial Intelligence
  1 Introduction = 1
    1.1 What is AI? = 1
      Acting humanly : The Turing Test approach = 2
      Thinking humanly : The cognitive modeling approach = 3
      Thinking rationally : The "laws of thought" approach = 4
      Acting rationally : The rational agent approach = 4
    1.2 The Foundations of Artificial Intelligence = 5
      Philosophy(428 B.C.-present) = 5
      Mathematics(c. 800-present) = 7
      Economics(1776-present) = 9
      Neuroscience(1861-present) = 10
      Psychology(1879-present) = 12
      Computer engineering(1940-present) = 14
      Control theory and Cybernetics(1948-present) = 15
      Linguistics(1957-present) = 16
    1.3 The History of Artificial Intelligence = 16
      The gestation of artificial intelligence(1943-1955) = 16
      The birth of artificial intelligence(1956) = 17
      Early enthusiasm, great expectations(1952-1969) = 18
      A dose of reality(1966-1973) = 21
      Knowledge-based systems : The key to power?(1969-1979) = 22
      AI becomes an industry(1980-present) = 24
      The return of neural networks(1986-present) = 25
      AI becomes a science(1987-present) = 25
      The emergence of intelligent agents(1995-present) = 27
    1.4 The State of the Art = 27
    1.5 Summary = 28
    Bibliographical and Historical Notes = 29
    Exercises = 30
  2 Intelligent Agents = 32
    2.1 Agents and Environments = 32
    2.2 Good Behavior : The Concept of Rationality = 34
      Performance measures = 35
      Rationality = 35
      Omniscience, learning, and autonomy = 36
    2.3 The Nature of Environments = 38
      Specifying the task environment = 38
      Properties of task environments = 40
    2.4 The Structure of Agents = 44
      Agent programs = 44
      Simple reflex agents = 46
      Model-based reflex agents = 48
      Goal-based agents = 49
      Utility-based agents = 51
      Learning agents = 51
    2.5 Summary = 54
    Bibliographical and Historical Notes = 55
    Exercises = 56
Ⅱ Problem-solving
  3 Solving Problems by Searching = 59
    3.1 Problem-Solving Agents = 59
      Well-defined problems and solutions = 62
      Formulating problems = 62
    3.2 Example Problems = 64
      Toy problems = 64
      Real-world problems = 67
    3.3 Searching for Solutions = 69
      Measuring problem-solving performance = 71
    3.4 Uninformed Search Strategies = 73
      Breadth-first search = 73
      Depth-first search = 75
      Depth-limited search = 77
      Iterative deepening depth-first search = 78
      Bidirectional search = 79
      Comparing uninformed search strategies = 81
    3.5 Avoiding Repeated States = 81
    3.6 Searching with Partial Information = 83
      Sensorless problems = 84
      Contingency problems = 86
    3.7 Summary = 87
    Bibliographical and Historical Notes = 88
    Exercises = 89
  4 Informed Search and Exploration = 94
    4.1 Informed(Heuristic) Search Strategies = 94
      Greedy best-first search = 95
       A* search : Minimizing the total estimated solution cost = 97
      Memory-bounded heuristic search = 101
      Learning to search better = 104
    4.2 Heuristic Functions = 105
      The effect of heuristic accuracy on performance = 106
      Inventing admissible heuristic functions = 107
      Learning heuristics from experience = 109
    4.3 Local Search Algorithms and Optimization Problems = 110
      Hill-climbing search = 111
      Simulated annealing search = 115
      Local beam search = 115
      Genetic algorithms = 116
    4.4 Local Search in Continuous Spaces = 119
    4.5 Online Search Agents and Unknown Environments = 122
      Online search problems = 123
      Online search agents = 125
      Online local search = 126
      Learning in online search = 127
    4.6 Summary = 129
    Bibliographical and Historical Notes = 130
    Exercises = 134
  5 Constraint Satisfaction Problems = 137
    5.1 Constraint Satisfaction Problems = 137
    5.2 Backtracking Search for CSPs = 141
      Variable and value ordering = 143
      Propagating information through constraints = 144
      Intelligent backtracking : looking backward = 148
    5.3 Local Search for Constraint Satisfaction Problems = 150
    5.4 The Structure of Problems = 151
    5.5 Summary = 155
    Bibliographical and Historical Notes = 156
    Exercises = 158
  6 Adversarial Search = 161
    6.1 Games = 161
    6.2 Optimal Decisions in Games = 162
      Optimal strategies = 163
      The minimax algorithm = 165
      Optimal decisions in multiplayer games = 165
    6.3 Alpha-Beta Pruning = 167
    6.4 Imperfect, Real-Time Decisions = 171
      Evaluation functions = 171
      Cutting off search = 173
    6.5 Games That Include an Element of Chance = 175
      Position evaluation in games with chance nodes = 177
      Complexity of expectiminimax = 177
      Card games = 179
    6.6 State-of-the-Art Game Programs = 180
    6.7 Discussion = 183
    6.8 Summary = 185
    Bibliographical and Historical Notes = 186
    Exercises = 189
Ⅲ Knowledge and reasoning
  7 Logical Agents = 194
    7.1 Knowledge-Based Agents = 195
    7.2 The Wumpus World = 197
    7.3 Logic = 200
    7.4 Prepositional Logic : A Very Simple Logic = 204
      Syntax = 204
      Semantics = 206
      A simple knowledge base = 208
      Inference = 208
      Equivalence, validity, and satisfiability = 210
    7.5 Reasoning Patterns in Propositional Logic = 211
      Resolution = 213
      Forward and backward chaining = 217
    7.6 Effective propositional inference = 220
      A complete backtracking algorithm = 221
      Local-search algorithms = 222
      Hard satisfiability problems = 224
    7.7 Agents Based on Propositional Logic = 225
      Finding pits and wumpuses using logical inference = 225
      Keeping track of location and orientation = 227
      Circuit-based agents = 227
      A comparison = 231
    7.8 Summary = 232
    Bibliographical and Historical Notes = 233
    Exercises = 236
  8 First-Order Logic = 240
    8.1 Representation Revisited = 240
    8.2 Syntax and Semantics of First-Order Logic = 245
      Models for first-order logic = 245
      Symbols and interpretations = 246
      Terms = 248
      Atomic sentences = 248
      Complex sentences = 249
      Quantifiers = 249
      Equality = 253
    8.3 Using First-Order Logic = 253
      Assertions and queries in first-order logic = 253
      The kinship domain = 254
      Numbers, sets, and lists = 256
      The wumpus world = 258
    8.4 Knowledge Engineering in First-Order Logic = 260
      The knowledge engineering process = 261
      The electronic circuits domain = 262
    8.5 Summary = 266
    Bibliographical and Historical Notes = 267
    Exercises = 268
  9 Inference in First-Order Logic = 272
    9.1 Propositional vs. First-Order Inference = 272
      Inference rules for quantifiers = 273
      Reduction to propositional inference = 274
    9.2 Unification and Lifting = 275
      A first-order inference rule = 275
      Unification = 276
      Storage and retrieval = 278
    9.3 Forward Chaining = 280
      First-order definite clauses = 280
      A simple forward-chaining algorithm = 281
      Efficient forward chaining = 283
    9.4 Backward Chaining = 287
      A backward chaining algorithm = 287
      Logic programming = 289
      Efficient implementation of logic programs = 290
      Redundant inference and infinite loops = 292
      Constraint logic programming = 294
    9.5 Resolution = 295
      Conjunctive normal form for first-order logic = 295
      The resolution inference rule = 297
      Example proofs = 297
      Completeness of resolution = 300
      Dealing with equality = 303
      Resolution strategies = 304
      Theorem provers = 306
    9.6 Summary = 310
    Bibliographical and Historical Notes = 310
    Exercises = 315
  10 Knowledge Representation = 320
    10.1 Ontological Engineering = 320
    10.2 Categories and Objects = 322
      Physical composition = 324
      Measurements = 325
      Substances and objects = 327
    10.3 Actions, Situations, and Events = 328
      The ontology of situation calculus = 329
      Describing actions in situation calculus = 330
      Solving the representational frame problem = 332
      Solving the inferential frame problem = 333
      Time and event calculus = 334
      Generalized events = 335
      Processes = 337
      Intervals = 338
      Fluents and objects = 339
    10.4 Mental Events and Mental Objects = 341
      A formal theory of beliefs = 341
      Knowledge and belief = 343
      Knowledge, time, and action = 344
    10.5 The Internet Shopping World = 344
      Comparing offers = 348
    10.6 Reasoning Systems for Categories = 349
      Semantic networks = 350
      Description logics = 353
    10.7 Reasoning with Default Information = 354
      Open and closed worlds = 354
      Negation as failure and stable model semantics = 356
      Circumscription and default logic = 358
    10.8 Truth Maintenance Systems = 360
    10.9 Summary = 362
    Bibliographical and Historical Notes = 363
    Exercises = 369
Ⅳ Planning
  11 Planning = 375
    11.1 The Planning Problem = 375
      The language of planning problems = 377
      Expressiveness and extensions = 378
      Example : Air cargo transport = 380
      Example : The spare tire problem = 381
      Example : The blocks world = 381
    11.2 Planning with State-Space Search = 382
      Forward state-space search = 382
      Backward state-space search = 384
      Heuristics for state-space search = 386
    11.3 Partial-Order Planning = 387
      A partial-order planning example = 391
      Partial-order planning with unbound variables = 393
      Heuristics for partial-order planning = 394
    11.4 Planning Graphs = 395
      Planning graphs for heuristic estimation = 397
      The GRAPHPLAN algorithm = 398
      Termination of GRAPHPLAN = 401
    11.5 Planning with Prepositional Logic = 402
      Describing planning problems in prepositional logic = 402
      Complexity of prepositional encodings = 405
    11.6 Analysis of Planning Approaches = 407
    11.7 Summary = 408
    Bibliographical and Historical Notes = 409
    Exercises = 412
  12 Planning and Acting in the Real World = 417
    12.1 Time, Schedules, and Resources = 417
      Scheduling with resource constraints = 420
    12.2 Hierarchical Task Network Planning = 422
      Representing action decompositions = 423
      Modifying the planner for decompositions = 425
      Discussion = 427
    12.3 Planning and Acting in Nondeterministic Domains = 430
    12.4 Conditional Planning = 433
      Conditional planning in fully observable environments = 433
      Conditional planning in partially observable environments = 437
    12.5 Execution Monitoring and Replanning = 441
    12.6 Continuous Planning = 445
    12.7 MultiAgent Planning = 449
      Cooperation : Joint goals and plans = 450
      Multibody planning = 451
      Coordination mechanisms = 452
      Competition = 454
    12.8 Summary = 454
    Bibliographical and Historical Notes = 455
    Exercises = 459
Ⅴ Uncertain knowledge and reasoning
  13 Uncertainty = 462
    13.1 Acting under Uncertainty = 462
      Handling uncertain knowledge = 463
      Uncertainty and rational decisions = 465
      Design for a decision-theoretic agent = 466
    13.2 Basic Probability Notation = 466
      Propositions = 467
      Atomic events = 468
      Prior probability = 468
      Conditional probability = 470
    13.3 The Axioms of Probability = 471
      Using the axioms of probability = 473
      Why the axioms of probability are reasonable = 473
    13.4 Inference Using Full Joint Distributions = 475
    13.5 Independence = 477
    13.6 Bayes' Rule and Its Use = 479
      Applying Bayes' rule : The simple case = 480
      Using Bayes' rule : Combining evidence = 481
    13.7 The Wumpus World Revisited = 483
    13.8 Summary = 486
    Bibliographical and Historical Notes = 487
    Exercises = 489
  14 Probabilistic Reasoning = 492
    14.1 Representing Knowledge in an Uncertain Domain = 492
    14.2 The Semantics of Bayesian Networks = 495
      Representing the full joint distribution = 495
      Conditional independence relations in Bayesian networks = 499
    14.3 Efficient Representation of Conditional Distributions = 500
    14.4 Exact Inference in Bayesian Networks = 504
      Inference by enumeration = 504
      The variable elimination algorithm = 507
      The complexity of exact inference = 509
      Clustering algorithms = 510
    14.5 Approximate Inference in Bayesian Networks = 511
      Direct sampling methods = 511
    14.6 Extending Probability to First-Order Representations = 519
    14.7 Other Approaches to Uncertain Reasoning = 523
      Rule-based methods for uncertain reasoning = 524
      Representing ignorance : Dempster-Shafer theory = 525
      Representing vagueness : Fuzzy sets and fuzzy logic = 526
    14.8 Summary = 528
    Bibliographical and Historical Notes = 528
    Exercises = 533
  15 Probabilistic Reasoning over Time = 537
    15.1 Time and Uncertainty = 537
      States and observations = 538
      Stationary processes and the Markov assumption = 538
    15.2 Inference in Temporal Models = 541
      Filtering and prediction = 542
      Smoothing = 544
      Finding the most likely sequence = 547
    15.3 Hidden Markov Models = 549
      Simplified matrix algorithms = 549
    15.4 Kalman Filters = 551
      Updating Gaussian distributions = 553
      A simple one-dimensional example = 554
      The general case = 556
      Applicability of Kalman filtering = 557
    15.5 Dynamic Bayesian Networks = 559
      Constructing DBNs = 560
      Exact inference in DBNs = 563
      Approximate inference in DBNs = 565
    15.6 Speech Recognition = 568
      Speech sounds = 570
      Words = 572
      Sentences = 574
      Building a speech recognizer = 576
    15.7 Summary = 578
    Bibliographical and Historical Notes = 578
    Exercises = 581
  16 Making Simple Decisions = 584
    16.1 Combining Beliefs and Desires under Uncertainty = 584
    16.2 The Basis of Utility Theory = 586
      Constraints on rational preferences = 586
      And then there was Utility = 588
    16.3 Utility Functions = 589
      The utility of money = 589
      Utility scales and utility assessment = 591
    16.4 Multiattribute Utility Functions = 593
      Dominance = 594
      Preference structure and multiattribute utility = 596
    16.5 Decision Networks = 597
      Representing a decision problem with a decision network = 598
      Evaluating decision networks = 599
    16.6 The Value of Information = 600
      A simple example = 600
      A general formula = 601
      Properties of the value of information = 602
      Implementing an information-gathering agent = 603
    16.7 Decision-Theoretic Expert Systems = 604
    16.8 Summary = 607
    Bibliographical and Historical Notes = 607
    Exercises = 609
  17 Making Complex Decisions = 613
    17.1 Sequential Decision Problems = 613
      An example = 613
      Optimality in sequential decision problems = 616
    17.2 Value Iteration = 618
      Utilities of states = 619
      The value iteration algorithm = 620
      Convergence of value iteration = 620
    17.3 Policy Iteration = 624
    17.4 Partially observable MDPs = 625
    17.5 Decision-Theoretic Agents = 629
    17.6 Decisions with Multiple Agents : Game Theory = 631
    17.7 Mechanism Design = 640
    17.8 Summary = 643
    Bibliographical and Historical Notes = 644
    Exercises = 646
Ⅵ Learning
  18 Learning from Observations = 649
    18.1 Forms of Learning = 649
    18.2 Inductive Learning = 651
    18.3 Learning Decision Trees = 653
      Decision trees as performance elements = 653
      Expressiveness of decision trees = 655
      Inducing decision trees from examples = 655
      Choosing attribute tests = 659
      Assessing the performance of the learning algorithm = 660
      Noise and overfilling = 661
      Broadening the applicability of decision trees = 663
    18.4 Ensemble Learning = 664
    18.5 Why Learning Works : Compulalional Learning Theory = 668
      How many examples are needed? = 669
      Learning decision lists = 670
      Discussion = 672
    18.6 Summary = 673
    Bibliographical and Historical Notes = 674
    Exercises = 676
  19 Knowledge in Learning = 678
    19.1 A Logical Formulation of Learning = 678
      Examples and hypotheses = 678
      Current-best-hypothesis search = 680
      Least-commitment search = 683
    19.2 Knowledge in Learning = 686
      Some simple examples = 687
      Some general schemes = 688
    19.3 Explanation-Based Learning = 690
      Extracting general rules from examples = 691
      Improving efficiency = 693
    19.4 Learning Using Relevance Information = 694
      Determining the hypothesis space = 695
      Learning and using relevance information = 695
    19.5 Inductive Logic Programming = 697
      An example = 699
      Top-down inductive learning methods = 701
      Inductive learning with inverse deduction = 703
      Making discoveries with inductive logic programming = 705
    19.6 Summary = 707
    Bibliographical and Historical Notes = 708
    Exercises = 710
  20 Statistical Learning Methods = 712
    20.1 Statistical Learning = 712
    20.2 Learning with Complete Data = 716
      Maximum-likelihood parameter learning : Discrete models = 716
      Naive Bayes models = 718
      Maximum-likelihood parameter learning : Continuous models = 719
      Bayesian parameter learning = 720
      Learning Bayes net structures = 722
    20.3 Learning with Hidden Variables : The EM Algorithm = 724
      Unsupervised clustering : Learning mixtures of Gaussians = 725
      Learning Bayesian networks with hidden variables = 727
      Learning hidden Markov models = 731
      The general form of the EM algorithm = 731
      Learning Bayes net structures with hidden variables = 732
    20.4 Instance-Based Learning = 733
      Nearest-neighbor models = 733
      Kernel models = 735
    20.5 Neural Networks = 736
      Units in neural networks = 737
      Network structures = 738
      Single layer feed-forward neural networks(perceptrons) = 740
      Multilayer feed-forward neural networks = 744
      Learning neural network structures = 748
    20.6 Kernel Machines = 749
    20.7 Case Study : Handwritten Digit Recognition = 752
    20.8 Summary = 754
    Bibliographical and Historical Notes = 755
    Exercises = 759
  21 Reinforcement Learning = 763
    21.1 Introduction = 763
    21.2 Passive Reinforcement Learning = 765
      Direct utility estimation = 766
      Adaptive dynamic programming = 767
      Temporal difference learning = 767
    21.3 Active Reinforcement Learning = 771
      Exploration = 771
      Learning an Action-Value Function = 775
    21.4 Generalization in Reinforcement Learning = 777
      Applications to game-playing = 780
      Application to robot control = 780
    21.5 Policy Search = 781
    21.6 Summary = 784
    Bibliographical and Historical Notes = 785
    Exercises = 788
Ⅶ Communicating, perceiving, and acting
  22 Communication = 790
    22.1 Communication as Action = 790
      Fundamentals of language = 791
      The component steps of communication = 792
    22.2 A Formal Grammar for a Fragment of English = 795
      The Lexicon of = 795
      The Grammar of = 796
    22.3 Syntactic Analysis(Parsing) = 798
      Efficient parsing = 800
    22.4 Augmented Grammars = 806
      Verb subcategorization = 808
      Generative capacity of augmented grammars = 809
    22.5 Semantic Interpretation = 810
      The semantics of an English fragment = 811
      Time and tense = 812
      Quantification = 813
      Pragmatic Interpretation = 815
      Language generation with DCGs = 817
    22.6 Ambiguity and Disambiguation = 818
      Disambiguation = 820
    22.7 Discourse Understanding = 821
      Reference resolution = 821
      The structure of coherent discourse = 823
    22.8 Grammar Induction = 824
    22.9 Summary = 826
    Bibliographical and Historical Notes = 827
    Exercises = 831
  23 Probabilistic Language Processing = 834
    23.1 Probabilistic Language Models = 834
      Probabilistic context-free grammars = 836
      Learning probabilities for PCFGs = 839
      Learning rule structure for PCFGs = 840
    23.2 Information Retrieval = 840
      Evaluating IR systems = 842
      IR refinements = 844
      Presentation of result sets = 845
      Implementing IR systems = 846
    23.3 Information Extraction = 848
    23.4 Machine Translation = 850
      Machine translation systems = 852
      Statistical machine translation = 853
      Learning probabilities for machine translation = 856
    23.5 Summary = 857
    Bibliographical and Historical Notes = 858
    Exercises = 861
  24 Perception = 863
    24.1 Introduction = 863
    24.2 Image Formation = 865
      Images without lenses : the pinhole camera = 865
      Lens systems = 866
      Light : the photometry of image formation = 867
      Color : the spectrophotometry of image formation = 868
    24.3 Early Image Processing Operations = 869
      Edge detection = 870
      Image segmentation = 872
    24.4 Extracting Three-Dimensional Information = 873
      Motion = 875
      Binocular stereopsis = 876
      Texture gradients = 879
      Shading = 880
      Contour = 881
    24.5 Object Recognition = 885
      Brightness-based recognition = 887
      Feature-based recognition = 888
      Pose Estimation = 890
    24.6 Using Vision for Manipulation and Navigation = 892
    24.7 Summary = 894
    Bibliographical and Historical Notes = 895
    Exercises = 898
  25 Robotics = 901
    25.1 Introduction = 901
    25.2 Robot Hardware = 903
      Sensors = 903
      Effectors = 904
    25.3 Robotic Perception = 907
      Localization = 908
      Mapping = 913
      Other types of perception = 915
    25.4 Planning to Move = 916
      Configuration space = 916
      Cell decomposition methods = 919
      Skeletonization methods = 922
    25.5 Planning uncertain movements = 923
      Robust methods = 924
    25.6 Moving = 926
      Dynamics and control = 927
      Potential field control = 929
      Reactive control = 930
    25.7 Robotic Software Architectures = 932
      Subsumption architecture = 932
      Three-layer architecture = 933
      Robotic programming languages = 934
    25.8 Application Domains = 935
    25.9 Summary = 938
    Bibliographical and Historical Notes = 939
    Exercises = 942
Ⅷ Conclusions
  26 Philosophical Foundations = 947
    26.1 Weak AI : Can Machines Act Intelligently? = 947
      The argument from disability = 948
      The mathematical objection = 949
      The argument from informality = 950
    26.2 Strong AI : Can Machines Really Think? = 952
      The mind-body problem = 954
      The "brain in a vat" experiment = 955
      The brain prosthesis experiment = 956
      The Chinese room = 958
    26.3 The Ethics and Risks of Developing Artificial Intelligence = 960
    26.4 Summary = 964
    Bibliographical and Historical Notes = 964
    Exercises = 967
  27 AI : Present and Future = 968
    27.1 Agent Components = 968
    27.2 Agent Architectures = 970
    27.3 Are We Going in the Right Direction? = 972
    27.4 What if AI Does Succeed? = 974
A Mathematical background = 977
  A.1 Complexity Analysis and O() Notation = 977
    Asymptotic analysis = 977
    NP and inherently hard problems = 978
  A.2 Vectors, Matrices, and Linear Algebra = 979
  A.3 Probability Distributions = 981
  Bibliographical and Historical Notes = 983
B Notes on Languages and Algorithms = 984
  B.1 Defining Languages with Backus-Naur Form(BNF) = 984
  B.2 Describing Algorithms with Pseudocode = 985
  B.3 Online Help = 985
Bibliography = 987
Index = 1045