Artificial intelligence / Elaine Rich, Kevin Knight.

By: Rich, Elaine .

Contributor(s): Knight, Kevin .

Material type: materialTypeLabel

BookPublisher: New York : McGraw-Hill, 1991Edition: 2nd ed.Description: xvii, 621 p. : ill. ; 25 cm.ISBN: 0070522634 .Subject(s): Artificial intelligence

DDC classification: 006.3

Contents:

What is artificial intelligence -- Problems, problem spaces and searches -- Heuristic search techniques -- Knowledge representation issues -- Using Predicate logic -- Representing knowledge using rules -- Symbolic reasoning under uncertainity -- Statistical reasoning -- Weak slot-and-filler structures -- Strong slot-and-filler structures -- Knowledge representation summary -- Game playing -- Planning -- Understanding -- Natural logic processing -- Parallel and distributed AI -- Learning -- Connectionist models -- Common sense -- Expert systems -- Perception and action -- Conclusion.

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode	Item holds
General Lending	MTU Bishopstown Library Store Item	006.3 (Browse shelf(Opens below))	1	Available		00158351
General Lending	MTU Bishopstown Library Store Item	006.3 (Browse shelf(Opens below))	1	Available		00020920

Total holds: 0

Enhanced descriptions from Syndetics:

A revision of an established text for undergraduate and postgraduate Artificial Intelligence courses, this text incorporates the latest research and methods.

Includes bibliographical references (p. 583-603) and index.

Table of contents provided by Syndetics

Preface (p. XV)
I Problems and Search (p. 1)
1 What Is Artificial Intelligence? (p. 3)
1.1 The AI Problems (p. 3)
1.2 The Underlying Assumption (p. 6)
1.3 What Is an AI Technique? (p. 8)
1.4 The Level of the Model (p. 22)
1.5 Criteria for Success (p. 24)
1.6 Some General References (p. 26)
1.7 One Final Word (p. 27)
1.8 Exercises (p. 28)
2 Problems, Problem Spaces, and Search (p. 29)
2.1 Defining the Problem as a State Space Search (p. 29)
2.2 Production Systems (p. 36)
2.3 Problem Characteristics (p. 44)
2.4 Production System Characteristics (p. 55)
2.5 Issues in the Design of Search Programs (p. 57)
2.6 Additional Problems (p. 60)
2.7 Summary (p. 61)
2.8 Exercises (p. 61)
3 Heuristic Search Techniques (p. 63)
3.1 Generate-and-Test (p. 64)
3.2 Hill Climbing (p. 65)
3.3 Best-First Search (p. 73)
3.4 Problem Reduction (p. 82)
3.5 Constraint Satisfaction (p. 88)
3.6 Means-Ends Analysis (p. 94)
3.7 Summary (p. 97)
3.8 Exercises (p. 98)
II Knowledge Representation (p. 103)
4 Knowledge Representation Issues (p. 105)
4.1 Representations and Mappings (p. 105)
4.2 Approaches to Knowledge Representation (p. 109)
4.3 Issues in Knowledge Representation (p. 115)
4.4 The Frame Problem (p. 126)
4.5 Summary (p. 129)
5 Using Predicate Logic (p. 131)
5.1 Representing Simple Facts in Logic (p. 131)
5.2 Representing Instance and Isa Relationships (p. 137)
5.3 Computable Functions and Predicates (p. 139)
5.4 Resolution (p. 143)
5.5 Natural Deduction (p. 164)
5.6 Summary (p. 165)
5.7 Exercises (p. 166)
6 Representing Knowledge Using Rules (p. 171)
6.1 Procedural versus Declarative Knowledge (p. 171)
6.2 Logic Programming (p. 173)
6.3 Forward versus Backward Reasoning (p. 177)
6.4 Matching (p. 182)
6.5 Control Knowledge (p. 188)
6.6 Summary (p. 192)
6.7 Exercises (p. 192)
7 Symbolic Reasoning under Uncertainty (p. 195)
7.1 Introduction to Nonmonotonic Reasoning (p. 195)
7.2 Logics for Nonmonotonic Reasoning (p. 199)
7.3 Implementation Issues (p. 208)
7.4 Augmenting a Problem Solver (p. 209)
7.5 Implementation: Depth-First Search (p. 211)
7.6 Implementation: Breadth-First Search (p. 222)
7.7 Summary (p. 226)
7.8 Exercises (p. 227)
8 Statistical Reasoning (p. 231)
8.1 Probability and Bayes' Theorem (p. 231)
8.2 Certainty Factors and Rule-Based Systems (p. 233)
8.3 Bayesian Networks (p. 239)
8.4 Dempster-Shafer Theory (p. 242)
8.5 Fuzzy Logic (p. 246)
8.6 Summary (p. 247)
8.7 Exercises (p. 248)
9 Weak Slot-and-Filler Structures (p. 251)
9.1 Semantic Nets (p. 251)
9.2 Frames (p. 257)
9.3 Exercises (p. 275)
10 Strong Slot-and-Filler Structures (p. 277)
10.1 Conceptual Dependency (p. 277)
10.2 Scripts (p. 284)
10.3 CYC (p. 288)
10.4 Exercises (p. 294)
11 Knowledge Representation Summary (p. 297)
11.1 Syntactic-Semantic Spectrum of Representation (p. 297)
11.2 Logic and Slot-and-Filler Structures (p. 299)
11.3 Other Representational Techniques (p. 301)
11.4 Summary of the Role of Knowledge (p. 302)
11.5 Exercises (p. 303)
III Advanced Topics (p. 305)
12 Game Playing (p. 307)
12.1 Overview (p. 307)
12.2 The Minimax Search Procedure (p. 310)
12.3 Adding Alpha-Beta Cutoffs (p. 314)
12.4 Additional Refinements (p. 319)
12.5 Iterative Deepening (p. 322)
12.6 References on Specific Games (p. 324)
12.7 Exercises (p. 326)
13 Planning (p. 329)
13.1 Overview (p. 329)
13.2 An Example Domain: The Blocks World (p. 332)
13.3 Components of a Planning System (p. 333)
13.4 Goal Stack Planning (p. 339)
13.5 Nonlinear Planning Using Constraint Posting (p. 347)
13.6 Hierarchical Planning (p. 354)
13.7 Reactive systems (p. 356)
13.8 Other Planning Techniques (p. 357)
13.9 Exercises (p. 357)
14 Understanding (p. 359)
14.1 What Is Understanding? (p. 359)
14.2 What Makes Understanding Hard? (p. 360)
14.3 Understanding as Constraint Satisfaction (p. 367)
14.4 Summary (p. 375)
14.5 Exercises (p. 375)
15 Natural Language Processing (p. 377)
15.1 Introduction (p. 379)
15.2 Syntactic Processing (p. 385)
15.3 Semantic Analysis (p. 397)
15.4 Discourse and Pragmatic Processing (p. 415)
15.5 Summary (p. 424)
15.6 Exercises (p. 426)
16 Parallel and Distributed AI (p. 429)
16.1 Psychological Modeling (p. 429)
16.2 Parallelism in Reasoning Systems (p. 430)
16.3 Distributed Reasoning Systems (p. 433)
16.4 Summary (p. 445)
16.5 Exercises (p. 445)
17 Learning (p. 447)
17.1 What Is Learning? (p. 447)
17.2 Rote Learning (p. 448)
17.3 Learning by Taking Advice (p. 450)
17.4 Learning in Problem Solving (p. 452)
17.5 Learning from Examples: Induction (p. 457)
17.6 Explanation-Based Learning (p. 471)
17.7 Discovery (p. 475)
17.8 Analogy (p. 479)
17.9 Formal Learning Theory (p. 482)
17.10 Neural Net Learning and Genetic Learning (p. 483)
17.11 Summary (p. 483)
17.12 Exercises (p. 484)
18 Connectionist Models (p. 487)
18.1 Introduction: Hopfield Networks (p. 488)
18.2 Learning in Neural Networks (p. 492)
18.3 Applications of Neural Networks (p. 514)
18.4 Recurrent Networks (p. 517)
18.5 Distributed Representations (p. 520)
18.6 Connectionist AI and Symbolic AI (p. 522)
18.7 Exercises (p. 525)
19 Common Sense (p. 529)
19.1 Qualitative Physics (p. 530)
19.2 Commonsense Ontologies (p. 533)
19.3 Memory Organization (p. 540)
19.4 Case-Based Reasoning (p. 543)
19.5 Exercises (p. 545)
20 Expert Systems (p. 547)
20.1 Representing and Using Domain Knowledge (p. 547)
20.2 Expert System Shells (p. 549)
20.3 Explanation (p. 550)
20.4 Knowledge Acquisition (p. 553)
20.5 Summary (p. 556)
20.6 Exercises (p. 557)
21 Perception and Action (p. 559)
21.1 Real-Time Search (p. 561)
21.2 Perception (p. 563)
21.3 Action (p. 569)
21.4 Robot Architectures (p. 573)
21.5 Summary (p. 576)
21.6 Exercises (p. 577)
22 Conclusion (p. 579)
22.1 Components of an AI Program (p. 579)
22.2 Exercises (p. 580)
References (p. 583)
Acknowledgements (p. 605)
Author Index (p. 607)
Subject Index (p. 613)

Artificial intelligence / Elaine Rich, Kevin Knight.

By: Rich, Elaine.

Contributor(s): Knight, Kevin.

Enhanced descriptions from Syndetics:

Table of contents provided by Syndetics

By: Rich, Elaine .

Contributor(s): Knight, Kevin .