人工智能：理论与实践（英文版）

　　本书是一本阐述人工智能基本理论及其实际应用的教材，由三位资深人工智能专家精心编著而成。针对机器智能系统开发中涌现同的表达与计算问题，本书介绍了最新的研究成果，并讨论了系统实现中涉及到的实际问题。作者深入探讨了用于解决学习、规划和不确定性问题的传统符号推理技术，以及神经网络、概率推理等新技术。书中出现的重要算法在每章后面都附有其LISP实现的源代码，以供您参考。本书还包含了按照语法，语义和计算复杂性对一些表达问题进行了分析。一致的真实世界的实例，展示了人工智能系统在机器人技术、制造业和计算机软件中的应用。关于自然语言、学习、规划和不确定性的一些综合章节融合了已有的方法和未来的发展方向。……

暂缺《人工智能：理论与实践（英文版）》作者简介

INTRODUCTION
Robot?Explorers,?2
1.1
Artificial?IntelLigence?in?Practice??3
Examples?of?Artificial?Intelligence?Systems,?4
1.2
Artificial?Intelligence?Theory?5
Examples?of?Artificial?Intelligence?Theory,?6
1.3
Identifying?and?Measuring?Intelligence
1.4
Computational?Theories?of?Behavior??9
Representation,?10
Syntax?and?Semantics,?11
1.5
Automated?Reasoning??12
Inference?and?Symbolic?Manipulation,?13
Representing?Common-Sense?Knowledge,?14
Combinatorial?Problems?and?Search,?14
Complexity?and?Expressivity,?15
1.6
How?This?Book?Is?Organized??16
Summary??18
Background??19
Exercises?20
SYMBOLIC?PROGRAMMING
2.1
Rule-Based?Reactive?System?Example?25
Representing?Sensors?and?Sensor?Values?as?Symbols,?26
2.2
Introduction?to?Lisp?27
Language?Requirements,27
Common?Lisp,?27
Lists?and?Lisp?Syntax,?28
Symbols,?28
Programs?and?Documentation,?28
2.3??Interacting?with?Lisp?29
The?Lisp?Interpreter,?29
2.4
Functions?in?Lisp??31
Function?Invocation,?31
Procedural?Abstraction,?32
Conditional?Statements,?33
Recursive?Functions,?35
Evaluating?Functions?in?Fa.?les,?35
2.5
Environments,?Symbols,?and?Scope?36
Assigning?Values?to?Symbols,?36
Eval?and?Apply?Revisited,?37
Structured?Environments,?38
Variables,?39
Lexical?Scoping,?40
2.6
More?on?Functions??42
Functions?with?Local?State,?42
Lambda?and?Functions?as?Arguments,?43
2.7
List?Processing?44
Suspending?Evaluation?Using?Quote,?44
Building?and?Accessing?Elements?in?Lists,?45
Lists?in?Memory,?45
Modifying?List?Structures?in?Memory,?46
Alternative?Parameter-Passing?Conventions,?47
Predicates?on?Lists,?48
Built-In?List?Manipulation?Functions,?48
Optional?Arguments,?49
List-Processing?Examples,?49
Data?Abstraction,?51
2.8
Iterative?Constructs??53
Mapping?Functions?to?Arguments,?53
General?Iteration,?54
Simple?Iteration,?55
2.9
Monitoring?and?Debugging?Programs?56
Tracing?and?Stepping?Through?Programs,?56
Formatted?Output,?58
2.10??Rule-Based?Reactive?System?Revisited??58
Summary?64
Background??65
Exercises?65
REPRESENTATION?AND?LOGIC
3.1
Propositional?Logic??73
Syntax?for?P,?74
Semantics?for?P,?75
32
Formal?System?for/v?76
Logical?Axioms?of?P,?77
Normal?Forms,?78
Rules?of?Inference,?79
Proofs?and?Theorems,?79
Resolution?Rule?of?Inference,?80
Completeness,?Soundness,?and?Decidability,?81
Computational?Complexity,?82
Solving?Problems?with?Logic,?82
3.3
Automated?Theorem?Proving?in?P?84
Goal?Reduction?in?P,?85
Proof?by?Contradiction,?87
3.4
Predicate?Calculus?88
Syntax?for?PC,?89
Translating?English?Sentences?into?Logic,?90
More?About?Quantification,?91
Semantics?for?PC,?91
3.5
Formal?System?for?PC?93
Specifying?Programs?in?Prolog,?94
Eliminating?Quantifiars,?94
Learning?and?Deductive?Inference,?96
Decidability,?98
3.6
Automated?Theorem?Proving?in?PC??99
Matching?and?Universal?Instantiation,?99
Goal?Reduction?in?PC,?101
Unification,?103
Concept?Description?Languages,?107
Semantic?Networks,?108
3.7
Nonmonotonic?Logic??109
Closed-World?Assumption,?109
Abductive?and?Default?Reasoning,?111
Minimal?Models,?112
3.8
Deductive?Retrieval?Systems??113
Forward?and?Backward?Chaining,?114
Reason?Maintenance?Systems,?116
Nonmonotonic?Data?Dependencies,?118
Summary?119
Background??121
Exercises??122
Lisp?Implementation:?Data?Dependencies??127
SEARCH
4.1
Basic?Search?Issues??133
Search?Spaces?and?Operators,?134
Appliance?Assembly?Example,?135
Exploiting?Structure?to?Expedite?Search,?136
4.2
Blind?Search?137
Depth-First?Search,?138
Depth-First?Search?Is?Space?Efficient,?139
Breadth-First?Search,?140
Breadth-First?Search?Is?Guaranteed,?141
Iterative-Deepening?Search,?141
Iterative-Deepening?Search?Is?Asymptotically?Optimal,?143
Searching?in?Graphs,?144
4.3
Heuristic?Search??144
Best-First?Search,?145
Admissible?Evaluation?Functions,?146
4.4
Optimization?and?Search??149
Hill-Climbing?Search,?149
Local?Minima?and?Maxima,?151
Gradient?Search,?153
Simulated?Annealing,?153
Simulated?Evolution?and?Genetic?Algorithms,?154
Application?to?Vehicle?Routing,?158
4.5
Adversary?Search??160
Minimax?Search,?160
a-B?Search,?163
4.6
Indexing?in?Discrimination?Trees??166
Storing?and?Retrieving?Predicate?Calculus?Formulas,?167
Decision?Trees,?168
Summary?169
Background??171
Exercises??171
Lisp?Implementation:?Discrimination?Trees??174
5?LEARNING
5.1
Classifying?Inductive?Learning?Problems??180
Supervised?Learning,?180
Classification?and?Concept?Learning,?182
Unsupervised?Learning,?183
Online?and?Batch?Learning?Methods,?183
52
Theory?of?Inductive?Inference??183
The?Role?of?Inductive?Bias,?184
Restricted?Hypothesis?Space?Biases,?184
Preference?Biases,?185
Probably?Approximately?Correct?Learning,?186
PAC?Learnable?Concept?Classes,?187
Finding?Consistent?Hypotheses,?188
5.3
Version?Spaces??188
Attributes,?Features,?and?Dimensions,?189
Specializing?and?Generalizing?Concepts,?190
Maintaining?Version-Space?Boundaries,?191
Data?Structures?for?Learning,?192
Implementing?the?Version-Space?Method,?194
Optimal?Method?for?Conjunctions?of?Positive?Literals,?195
5.4
Decision?Trees??195
Implementing?a?Preference?for?Small?Decision?Trees,?196
Disorder?and?Information?Theory,?199
Decision?Trees?in?Practice,?202
5.5
Network?Learning?Methods?202
Model?for?Computation?in?Biological?Systems,?2113
Adjustable?Weights?and?Restricted?Hypothesis?Spaces,?205
5.6
Gradient?Guided?Search?206
Searching?in?Linear?Function?Spaces,?207
Experimental?Validation,?208
Nonlinear?Function?Spaces?and?Artificial?Neural
Networks,?210
Deriving?the?Gradient?for?Multilayer?Networks,?211
Error?Backpropagation?Procedure,?212
Implementing?Artificial?Neural?Networks?in?Lisp,?214
Representational?and?Computational?Issues,?217
Networks?with?Adjustable?Thresholds,?218
Comparing?the?Performance?of?Different?Networks,?220
5.7
Perceptrons??221
Perceptron?Learning?Rule,?222
Linearly?Separable?Funct/ons,?223
5.8
Radial?Basis?Functions??224
Approximating?Functions?by?Combining?Gaussians,?225
Two-Step?Strategy?for?Adjusting?Weights,?227
Functions?with?Multidimensional?Input?Spaces,?230
5.9
Learning?in?Dynamic?Environments??231
Reinforcement?Learning.?231
Computing?an?Optimal?Policy,?235
Online?Methods?for?Learning?Value?Functions,?235
Learning?by?Exploration.?239
Summary?24O
Background??242
Exercises??243
Lisp?Implementation:?Learning?Algorithms?249
6??ADVANCED?REPRESENTATION
6.1??Temporal?Reasoning??256
6.2
The?Situation?Calculus?257
Constraining?Fluents?in?Situations,?260
Frame?Problem,?260
Qualification?Problem,?262
6.3
First-Order?Interval?Temporal?Logic?264
Syntax?for?the?Interval?Logic,?265
Representing?Change?in?the?Interval?Logic,?267
Semantics?for?the?Interval?Logic,?268
6.4
Managing?Temporal?Knowledge??269
6.5
Knowledge?and?Belief??273
Possible-Worlds?Semantics,?277
6.6
Spatial?Reasoning?279
Representing?Spatial?Knowledge,?279
Planning?Paths?in?Configuration?Space,?281
Path?Planning?as?Graph?Search,?282
Locally?Distinctive?Places,?285
Summary?286
Background??287
Exercises??288
Lisp?Implementation:?Temporal?Reasoning?291
PLANNING
7.1
State-Space?Search?298
What?is?Planning?,?298
Planning?as?.%arch,?300
Representing?and?Solving?Search?Problems,?301
State?Progression,?3O2
Goal?Regression,?303
Means/Ends?Analysis,
Machine?Assembly?Example,?305
Operant?Schemas,?306
Block-Stacking?Problems,?307
7.2
Least?Commitment?Planning?308
Search?in?the?Space?of?Partially?Ordered?Plans,?309
Sound,?Complete,?andSystematic?Search,?312
Block-Stacking?Example,?313
Recognizing?and?Resolving?Conflicts,?316
Variables?in?Par''dally?Ordered?Plans,?317
7.3
Planning?in?a?Hierarchy?of?Abstraction?Spaces??320
Analysis?of?Planning?with?levels?of?Abstraction,?321
Towers-of-Hanoi?Problems,?322
Task?Reduction?Planning,?325
7.4
Adapting?Previously?Generated?Plans?326
IndexLng,?Retrieving,?and?Adapting?Plans,?326
Analysis?of?Adaptive?Planning,?331
7.5
Planning?with?Incomplete?Information?332
The?Copier-Repair?Problem,?332
Generating?Conditional?Plans,?335
Contexts?Represent?Possible?Sets?of?Observations,?336
7.6
More?Expressive?Models?of?Action??340
Conditional?Effects,?341
isjunctive?Preconditions,?342
Universally?Quantified?Effects,?343
Wandering?Briefcase?Example,?344
Processes?Outside?the?Planner''s?Control,?345
Summary?346
Background??347
Exercises??348
Lisp?Implementation:?Refining?Partially?Ordered
Plans?351
UNCERTAINTY
8.1
Motivation?for?Reasoning?Under?Uncertainty??357
Sources?of?Uncertainty,?357
Representing?Uncertain?Knowledge,?357
Applications?Involving?Uncertainty,?358
8.2
Probability?Theory??359
Frequency?Interpretation?of?Probability,?359
Save?Interpretation?of?Probability,?359
Desrees?of?Belief,?360
Random?Variables?and?Distributions,?361
Conditional?Probability,?362
Calculus?for?Combining?Probabilities,
Conclitional?Independence,?366
Maintaining?Consistency,?367
8.3
Probabilisfic?Networks?368
Graphical?Models,?369
Path-Based?Characterization?of?Independence,?371
Quantifying?Probabilistic?Networks,?372
Inference?in?Probabflistic?Networks,?373
Exact?Inference?in?Tree-Structured?Networks,?374
Propagating?Evidence?in?Trees,?378
Exact?Inference?in?Singly?Connected?Networks,?380
Approximate?Inference?Using?Stochastic?Simulation,?382
Likelihood-Weighting?Algorithm,?384
Probabilistic?Reasoning?in?Medicine,?386
8.4
Decision?Theory??388
Preferences?and?Utilities,?389
Decision?Tree?Methods,?390
Computing?the?Value?of?Information,?393
Automated?Decision?Making?in?Medidne,?394
Summary??395
Background??396
Exercises?397
Lisp?Implementation:?Inference?in?Probabilistic
Networks??399
9??IMAGE?UNDERSTANDING
9.1
Sensors?and?Images?410
Digital?Images,?410
Noise?in?Image?Processing,?410
9.2
Computer?Vision?412
Understanding?Images,?413
Vision?Versus?Thought,?414
9.3
Human?Vision??415
Transferring?Information?from?the?Eye?to?the?Brain,?415
Compressing?Visit?Information,?417
9.4
Vision?as?a?Recovery?Problem??418
What?to?Recover,?420
Geometric?Aspects?of?Image?Formation,?420
Perspective?Projection,?420
Orthographic?Projection,?423
Paraperspective?Projection,?425
Shape?Representation,?426
Surface?Orientation?and?Shape?Under?Perspective,?426
Surface?Orientation?and?Shape?Under?Orthography,?426
Stereographic?Projection,?427
Geometric?Properties?of?the?Perspective?Projection,?427
Imaging?with?tenses,?430
Photometric?Aspects?of?Image?Formation,?430
9.5
Recovery?of?Image?Descriptions??431
Edge?Detection,?431
Differentiation?Approaches,?432
Model-Based?Approaches,?436
Edge?Grouping?and?Hough?Transform,?437
Image?Segmentation,?438
9.6
Shape?from?Contour?440
Qualitative?Analysis?Using?Edge?Labels,?441
Quantitative?Analysis?Using?Skewed?Symmetries,?442
9.7??Shape?from?Shading?444
Reflectance?Maps,?445
Solving?Ill-Posed?Problems,?448
Photometric?Stereo,?449
9.8
Shape?from?Texture?450
Density?of?Textural?Elements,?450
Textural?Reflectance?Maps,?451
9.9
Stereo??453
Addressing?the?Correspondence?Problem,?453
Intensity-Based?Matching,?455
Edge-Based?Matching,?456
9.10??Analysis?of?Visual?Motion??457
Motion?Fields,?458
Motion?Field?Estimation,?460
Motion?Field?Interpretation,?463
9.11??Active?Vision??465
9.12??Applications??466
Autonomous?Vehicle?Navigation,?467
Object?Recognition,?469
Summary?471
Background?474
Exercises?476
Lisp?Implementation:?Labeling?Polyhedral?Scenes
10??NATURAL?LANGUAGE?PROCESSING
10.1??Components?of?Language?491
Ccmtent?and?Function?Words,?491
Structure?of?Phrases,?492
10.2??Context-Free?Grammars?493
Parsing,?495
10.3??Parsing?Context-Free?Grammars??496
Exploiting?the?Lexicon,?498
Building?a?Parse?Tree,?499
10.4??Grammars?Involving?Features??502
Matching?with?Features,?5O5
10.5??Efficient?Parsing?with?Charts?507
Ambiguous?Sentences,?507
10.6??Semantic?Interpretation?511
Word?Senses,?512
Semantic?Interpretation?Using?Features,?515
Disambiguating?Word?Senses,?517
10.7??Generating?Natural?Language??519
10.8??Natural?Language?in?Context?521
Speech?Acts,?521
Establishing?Reference,?522
Handling?Database?Assertions?and?Queries,?524
10.9?Quantifier?Scoping?529
Summary?530
Background??530
Exercises??531
Lisp?Implementation:?Simple?Parser?533
BIBLIOGRAPHY
VOCABULARY?INDEX
CODE?INDEX