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Tables of Contents for Artificial Intelligence
Chapter/Section Title
Page #
Page Count
Preface
xix
1 Introduction
1
18
1.1 What Is AI?
1
5
1.2 Approaches to Artificial Intelligence
6
2
1.3 Brief History of AI
8
3
1.4 Plan of the Book
11
3
1.5 Additional Readings and Discussion
14
3
Exercises
17
2
I Reactive Machines
19
96
2 Stimulus-Response Agents
21
16
2.1 Perception and Action
21
6
2.1.1 Perception
24
1
2.1.2 Action
24
1
2.1.3 Boolean Algebra
25
1
2.1.4 Classes and Forms of Boolean Functions
26
1
2.2 Representing and Implementing Action Functions
27
6
2.2.1 Production Systems
27
2
2.2.2 Networks
29
3
2.2.3 The Subsumption Architecture
32
1
2.3 Additional Readings and Discussion
33
1
Exercises
34
3
3 Neural Networks
37
22
3.1 Introduction
37
1
3.2 Training Single TLUs
38
6
3.2.1 TLU Geometry
38
1
3.2.2 Augmented Vectors
39
1
3.2.3 Gradient Descent Methods
39
2
3.2.4 The Windrow-Hoff Procedure
41
1
3.2.5 The Generalized Delta Procedure
41
2
3.2.6 The Error-Correction Procedure
43
1
3.3 Neural Networks
44
7
3.3.1 Motivation
44
1
3.3.2 Notation
45
1
3.3.3 The Backpropagation Method
46
2
3.3.4 Computing Weight Changes in the Final Layer
48
1
3.3.5 Computing Changes to the Weights in Intermediate Layers
48
3
3.4 Generalization, Accuracy, and Overfitting
51
3
3.5 Additional Readings and Discussion
54
1
Exercises
55
4
4 Machine Evolution
59
12
4.1 Evolutionary Computation
59
1
4.2 Genetic Programming
60
9
4.2.1 Program Representation in GP
60
2
4.2.2 The GP Process
62
3
4.2.3 Evolving a Wall-Following Robot
65
4
4.3 Additional Readings and Discussion
69
1
Exercises
69
2
5 State Machines
71
14
5.1 Representing the Environment by Feature Vectors
71
2
5.2 Elman Networks
73
1
5.3 Iconic Representations
74
3
5.4 Blackboard Systems
77
3
5.5 Additional Readings and Discussion
80
1
Exercises
80
5
6 Robot Vision
85
30
6.1 Introduction
85
1
6.2 Steering an Automobile
86
2
6.3 Two Stages of Robot Vision
88
3
6.4 Image Processing
91
11
6.4.1 Averaging
91
2
6.4.2 Edge Enhancement
93
3
6.4.3 Combining Edge Enhancement with Averaging
96
1
6.4.4 Region Finding
97
4
6.4.5 Using Image Attributes Other Than Intensity
101
1
6.5 Scene Analysis
102
6
6.5.1 Interpreting Lines and Curves in the Image
103
3
6.5.2 Model-Based Vision
106
2
6.6 Stereo Vision and Depth Information
108
2
6.7 Additional Readings and Discussion
110
1
Exercises
111
4
II Search in State Spaces
115
100
7 Agents That Plan
117
12
7.1 Memory Versus Computation
117
1
7.2 State-Space Graphs
118
3
7.3 Searching Explicit State Spaces
121
1
7.4 Feature-Based State Spaces
122
2
7.5 Graph Notation
124
1
7.6 Additional Readings and Discussion
125
1
Exercises
126
3
8 Uninformed Search
129
10
8.1 Formulating the State Space
129
1
8.2 Components of Implicit State-Space Graphs
130
1
8.3 Breadth-First Search
131
2
8.4 Depth-First or Backtracking Search
133
2
8.5 Iterative Deepening
135
1
8.6 Additional Readings and Discussion
136
1
Exercises
137
2
9 Heuristic Search
139
24
9.1 Using Evaluation Functions
139
2
9.2 A General Graph-Searching Algorithm
141
14
9.2.1 Algorithm A*
142
3
9.2.2 Admissibility of A*
145
5
9.2.3 The Consistency (or Monotone) Condition
150
3
9.2.4 Iterative-Deepening A*
153
1
9.2.5 Recursive Best-First Search
154
1
9.3 Heuristic Functions and Search Efficiency
155
5
9.4 Additional Readings and Discussion
160
1
Exercises
160
3
10 Planning, Acting, and Learning
163
18
10.1 The Sense/Plan/Act Cycle
163
2
10.2 Approximate Search
165
7
10.2.1 Island-Driven Search
166
1
10.2.2 Hierarchical Search
167
2
10.2.3 Limited-Horizon Search
169
1
10.2.4 Cycles
170
1
10.2.5 Building Reactive Procedures
170
2
10.3 Learning Heuristic Functions
172
3
10.3.1 Explicit Graphs
172
1
10.3.2 Implicit Graphs
173
2
10.4 Rewards Instead of Goals
175
2
10.5 Additional Readings and Discussion
177
1
Exercises
178
3
11 Alternative Search Formulations and Applications
181
14
11.1 Assignment Problems
181
2
11.2 Constructive Methods
183
4
11.3 Heuristic Repair
187
2
11.4 Function Optimization
189
3
Exercises
192
2
12 Adversarial Search
195
20
12.1 Two-Agent Games
195
2
12.2 The Minimax Procedure
197
5
12.3 The Alpha-Beta Procedure
202
5
12.4 The Search Efficiency of the Alpha-Beta Procedure
207
1
12.5 Other Important Matters
208
1
12.6 Games of Chance
208
2
12.7 Learning Evaluation Functions
210
2
12.8 Additional Readings and Discussion
212
1
Exercises
213
2
III Knowledge Representation and Reasoning
215
146
13 The Propositional Calculus
217
14
13.1 Using Constraints on Feature Values
217
2
13.2 The Language
219
1
13.3 Rules of Inference
220
1
13.4 Definition of Proof
221
1
13.5 Semantics
222
4
13.5.1 Interpretations
222
1
13.5.2 The Propositional Truth Table
223
1
13.5.3 Satisfiability and Models
224
1
13.5.4 Validity
224
1
13.5.5 Equivalence
225
1
13.5.6 Entailment
225
1
13.6 Soundness and Completeness
226
1
13.7 The PSAT Problem
227
1
13.8 Other Important Topics
228
1
13.8.1 Language Distinctions
228
1
13.8.2 Metatheorems
228
1
13.8.3 Associative Laws
229
1
13.8.4 Distributive Laws
229
1
Exercises
229
2
14 Resolution in the Propositional Calculus
231
8
14.1 A New Rule of Inference: Resolution
231
1
14.1.1 Clauses as wffs
231
1
14.1.2 Resolution on Clauses
231
1
14.1.3 Soundness of Resolution
232
1
14.2 Converting Arbitrary wffs to Conjunctions of Clauses
232
1
14.3 Resolution Refutations
233
2
14.4 Resolution Refutation Search Strategies
235
2
14.4.1 Ordering Strategies
235
1
14.4.2 Refinement Strategies
236
1
14.5 Horn Clauses
237
1
Exercises
238
1
15 The Predicate Calculus
239
14
15.1 Motivation
239
1
15.2 The Language and Its Syntax
240
1
15.3 Semantics
241
4
15.3.1 Worlds
241
1
15.3.2 Interpretations
242
1
15.3.3 Models and Related Notions
243
1
15.3.4 Knowledge
244
1
15.4 Quantification
245
1
15.5 Semantics of Quantifiers
246
2
15.5.1 Universal Quantifiers
246
1
15.5.2 Existential Quantifiers
247
1
15.5.3 Useful Equivalences
247
1
15.5.4 Rules of Inference
247
1
15.6 Predicate Calculus as a Language for Representing Knowledge
248
2
15.6.1 Conceptualizations
248
1
15.6.2 Examples
248
1
15.7 Additional Readings and Discussion
250
1
Exercises
250
3
16 Resolution in the Predicate Calculus
253
16
16.1 Unification
253
3
16.2 Predicate-Calculus Resolution
256
1
16.3 Completeness and Soundness
257
1
16.4 Converting Arbitrary wffs to Clause Form
257
3
16.5 Using Resolution to Prove Theorems
260
1
16.6 Answer Extraction
261
1
16.7 The Equality Predicate
262
3
16.8 Additional Readings and Discussion
265
1
Exercises
265
4
17 Knowledge-Based Systems
269
32
17.1 Confronting the Real World
269
1
17.2 Reasoning Using Horn Clauses
270
5
17.3 Maintenance in Dynamic Knowledge Bases
275
5
17.4 Rule-Based Expert Systems
280
6
17.5 Rule Learning
286
11
17.5.1 Learning Propositional Calculus Rules
286
5
17.5.2 Learning First-Order Logic Rules
291
4
17.5.3 Explanation-Based Generalization
295
2
17.6 Additional Readings and Discussion
297
1
Exercises
298
3
18 Representing Commonsense Knowledge
301
16
18.1 The Commonsense World
301
5
18.1.1 What Is Commonsense Knowledge?
301
2
18.1.2 Difficulties in Representing Commonsense Knowledge
303
1
18.1.3 The Importance of Commonsense Knowledge
304
1
18.1.4 Research Areas
305
1
18.2 Time
306
2
18.3 Knowledge Representation by Networks
308
5
18.3.1 Taxonomic Knowledge
308
1
18.3.2 Semantic Networks
309
1
18.3.3 Nonmonotonic Reasoning in Semantic Networks
309
3
18.3.4 Frames
312
1
18.4 Additional Readings and Discussion
313
1
Exercises
314
3
19 Reasoning with Uncertain Information
317
26
19.1 Review of Probability Theory
317
6
19.1.1 Fundamental Ideas
317
3
19.1.2 Conditional Probabilities
320
3
19.2 Probabilistic Inference
323
2
19.2.1 A General Method
323
1
19.2.2 Conditional Independence
324
1
19.3 Bayes Networks
325
3
19.4 Patterns of Inference in Bayes Networks
328
1
19.5 Uncertain Evidence
329
1
19.6 D-Separation
330
2
19.7 Probabilistic Inference in Polytrees
332
6
19.7.1 Evidence Above
332
2
19.7.2 Evidence Below
334
2
19.7.3 Evidence Above and Below
336
1
19.7.4 A Numerical Example
336
2
19.8 Additional Readings and Discussion
338
1
Exercises
339
4
20 Learning and Acting with Bayes Nets
343
18
20.1 Learning Bayes Nets
343
8
20.1.1 Known Network Structure
343
3
20.1.2 Learning Network Structure
346
5
20.2 Probabilistic Inference and Action
351
7
20.2.1 The General Setting
351
1
20.2.2 An Extended Example
352
4
20.2.3 Generalizing the Example
356
2
20.3 Additional Readings and Discussion
358
1
Exercises
358
3
IV Planning Methods Based on Logic
361
44
21 The Situation Calculus
363
10
21.1 Reasoning about States and Actions
363
4
21.2 Some Difficulties
367
2
21.2.1 Frame Axioms
367
2
21.2.2 Qualifications
369
1
21.2.3 Ramifications
369
1
21.3 Generating Plans
369
1
21.4 Additional Readings and Discussion
370
1
Exercises
371
2
22 Planning
373
32
22.1 STRIPS Planning Systems
373
12
22.1.1 Describing States and Goals
373
1
22.1.2 Forward Search Methods
374
2
22.1.3 Recursive STRIPS
376
3
22.1.4 Plans with Run-Time Conditionals
379
1
22.1.5 The Sussman Anomaly
380
1
22.1.6 Backward Search Methods
381
4
22.2 Plan Spaces and Partial-Order Planning
385
8
22.3 Hierarchical Planning
393
3
22.3.1 ABSTRIPS
393
2
22.3.2 Combining Hierarchical and Partial-Order Planning
395
1
22.4 Learning Plans
396
2
22.5 Additional Readings and Discussion
398
2
Exercises
400
5
V Communication and Integration
405
48
23 Multiple Agents
407
14
23.1 Interacting Agents
407
1
23.2 Models of Other Agents
408
4
23.2.1 Varieties of Models
408
2
23.2.2 Simulation Strategies
410
1
23.2.3 Simulated Databases
410
1
23.2.4 The Intentional Stance
411
1
23.3 A Modal Logic of Knowledge
412
5
23.3.1 Modal Operators
412
1
23.3.2 Knowledge Axioms
413
2
23.3.3 Reasoning about Other Agents' Knowledge
415
2
23.3.4 Predicting Actions of Other Agents
417
1
23.4 Additional Readings and Discussion
417
1
Exercises
418
3
24 Communication among Agents
421
22
24.1 Speech Acts
421
4
24.1.1 Planning Speech Acts
423
1
24.1.2 Implementing Speech Acts
423
2
24.2 Understanding Language Strings
425
10
24.2.1 Phrase-Structure Grammars
425
3
24.2.2 Semantic Analysis
428
4
24.2.3 Expanding the Grammar
432
3
24.3 Efficient Communication
435
2
24.3.1 Use of Context
435
1
24.3.2 Use of Knowledge to Resolve Ambiguities
436
1
24.4 Natural Language Processing
437
3
24.5 Additional Readings and Discussion
440
1
Exercises
440
3
25 Agent Architectures
443
10
25.1 Three-Level Architectures
444
2
25.2 Goal Arbitration
446
2
25.3 The Triple-Tower Architecture
448
1
25.4 Bootstrapping
449
1
25.5 Additional Readings and Discussion
450
1
Exercises
450
3
Bibliography
453
40
Index
493