Artificial intelligence : structures and strategies for complex problem solving.

Enhanced descriptions from Syndetics:

Much has changed since the early editions of Artificial Intelligence were published. To reflect this the introductory material of this fifth edition has been substantially revised and rewritten to capture the excitement of the latest developments in AI work. Artificial intelligence is a diverse field. To ask the question "what is intelligence?" is to invite as many answers as there are approaches to the subject of artificial intelligence. These could be intelligent agents, logical reasoning, neural networks, expert systems, evolutionary computing and so on. This fifth edition covers all the main strategies used for creating computer systems that will behave in "intelligent" ways. It combines the broadest approach of any text in the marketplace with the practical information necessary to implement the strategies discussed, showing how to do this through Prolog or LISP programming.

Table of contents provided by Syndetics

• Preface (p. vii)
• Publisher's Acknowledgements (p. xv)
• Part I Artificial Intelligence: Its Roots and Scope (p. 1)
• 1 Al: History and Applications (p. 3)
• 1.1 From Eden to ENIAC: Attitudes toward Intelligence, Knowledge, and Human Artifice (p. 3)
• 1.2 Overview of AI Application Areas (p. 17)
• 1.3 Artificial Intelligence--A Summary (p. 28)
• 1.4 Epilogue and References (p. 29)
• 1.5 Exercises (p. 31)
• Part II Artificial Intelligence as Representation and Search (p. 33)
• 2 The Predicate Calculus (p. 47)
• 2.0 Introduction (p. 47)
• 2.1 The Propositional Calculus (p. 47)
• 2.2 The Predicate Calculus (p. 52)
• 2.3 Using Inference Rules to Produce Predicate Calculus Expressions (p. 64)
• 2.4 Application: A Logic-Based Financial Advisor (p. 75)
• 2.5 Epilogue and References (p. 79)
• 2.6 Exercises (p. 79)
• 3 Structures and Strategies for State Space Search (p. 81)
• 3.0 Introduction (p. 81)
• 3.1 Graph Theory (p. 84)
• 3.2 Strategies for State Space Search (p. 93)
• 3.3 Using the State Space to Represent Reasoning with the Predicate Calculus (p. 107)
• 3.4 Epilogue and References (p. 121)
• 3.5 Exercises (p. 121)
• 4 Heuristic Search (p. 123)
• 4.0 Introduction (p. 123)
• 4.1 An Algorithm for Heuristic Search (p. 127)
• 4.2 Admissibility, Monotonicity, and Informedness (p. 139)
• 4.3 Using Heuristics in Games (p. 144)
• 4.4 Complexity Issues (p. 152)
• 4.5 Epilogue and References (p. 156)
• 4.6 Exercises (p. 156)
• 5 Control and Implementation of State Space Search (p. 159)
• 5.0 Introduction (p. 159)
• 5.1 Recursion-Based Search (p. 160)
• 5.2 Pattern-Directed Search (p. 164)
• 5.3 Production Systems (p. 171)
• 5.4 The Blackboard Architecture for Problem Solving (p. 187)
• 5.5 Epilogue and References (p. 189)
• 5.7 Exercises (p. 190)
• Part III Representation and Intelligence: The AI Challenge (p. 193)
• 6 Knowledge Representation (p. 197)
• 6.0 Issues in Knowledge Representation (p. 197)
• 6.1 A Brief History of AI Representational Systems (p. 198)
• 6.2 Conceptual Graphs: A Network Language (p. 218)
• 6.3 Alternatives to Explicit Representation (p. 228)
• 6.4 Agent Based and Distributed Problem Solving (p. 235)
• 6.5 Epilogue and References (p. 240)
• 6.6 Exercises (p. 243)
• 7 Strong Method Problem Solving (p. 247)
• 7.0 Introduction (p. 247)
• 7.1 Overview of Expert System Technology (p. 249)
• 7.2 Rule-Based Expert Systems (p. 256)
• 7.3 Model-Based, Case Based, and Hybrid Systems (p. 268)
• 7.4 Planning (p. 284)
• 7.5 Epilogue and References (p. 299)
• 7.6 Exercises (p. 301)
• 8 Reasoning in Uncertain Situations (p. 303)
• 8.0 Introduction (p. 303)
• 8.1 Logic-Based Abductive Inference (p. 305)
• 8.2 Abduction: Alternatives to Logic (p. 320)
• 8.3 The Stochastic Approach to Uncertainty (p. 333)
• 8.4 Epilogue and References (p. 344)
• 8.5 Exercises (p. 346)
• Part IV Machine Learning (p. 349)
• 9 Machine Learning: Symbol-based (p. 351)
• 9.0 Introduction (p. 603)
• 9.1 A Framework for Symbol-based Learning (p. 354)
• 9.2 Version Space Search (p. 360)
• 9.3 The ID3 Decision Tree Induction Algorithm (p. 372)
• 9.4 Inductive Bias and Learnability (p. 381)
• 9.5 Knowledge and Learning (p. 386)
• 9.6 Unsupervised Learning (p. 397)
• 9.7 Reinforcement Learning (p. 406)
• 9.8 Epilogue and References (p. 413)
• 9.9 Exercises (p. 414)
• 10 Machine Learning: Connectionist (p. 417)
• 10.0 Introduction (p. 417)
• 10.1 Foundations for Connectionist Networks (p. 419)
• 10.2 Perceptron Learning (p. 422)
• 10.3 Backpropagation Learning (p. 431)
• 10.4 Competitive Learning (p. 438)
• 10.5 Hebbian Coincidence Learning (p. 446)
• 10.6 Attractor Networks or "Memories" (p. 457)
• 10.7 Epilogue and References (p. 467)
• 10.8 Exercises (p. 468)
• 11 Machine Learning: Social and Emergent (p. 469)
• 11.0 Social and Emergent Models of Learning (p. 469)
• 11.1 The Genetic Algorithm (p. 471)
• 11.2 Classifier Systems and Genetic Programming (p. 481)
• 11.3 Artificial Life and Society-Based Learning (p. 492)
• 11.4 Epilogue and References (p. 503)
• 11.5 Exercises (p. 504)
• Part V Advanced Topics for AI Problem Solving (p. 507)
• 12 Automated Reasoning (p. 509)
• 12.0 Introduction to Weak Methods in Theorem Proving (p. 509)
• 12.1 The General Problem Solver and Difference Tables (p. 510)
• 12.2 Resolution Theorem Proving (p. 516)
• 12.3 PROLOG and Automated Reasoning (p. 537)
• 12.4 Further Issues in Automated Reasoning (p. 543)
• 12.5 Epilogue and References (p. 550)
• 12.6 Exercises (p. 551)
• 13 Understanding Natural Language (p. 553)
• 13.0 Role of Knowledge in Language Understanding (p. 553)
• 13.1 Deconstructing Language: A Symbolic Analysis (p. 556)
• 13.2 Syntax (p. 559)
• 13.3 Syntax and Knowledge with ATN Parsers (p. 568)
• 13.4 Stochastic Tools for Language Analysis (p. 578)
• 13.5 Natural Language Applications (p. 585)
• 13.6 Epilogue and References (p. 592)
• 13.7 Exercises (p. 557)
• Part VI Languages and Programming Techniques for Artificial Intelligence (p. 597)
• 14 An Introduction to Prolog (p. 603)
• 14.0 Introduction (p. 603)
• 14.1 Syntax for Predicate Calculus Programming (p. 604)
• 14.2 Abstract Data Types (ADTs) in PROLOG (p. 616)
• 14.3 A Production System Example in PROLOG (p. 620)
• 14.4 Designing Alternative Search Strategies (p. 625)
• 14.5 A PROLOG Planner (p. 630)
• 14.6 PROLOG: Meta-Predicates, Types, and Unification (p. 633)
• 14.7 Meta-Interpreters in PROLOG (p. 641)
• 14.8 Learning Algorithms in PROLOG (p. 656)
• 14.9 Natural Language Processing in PROLOG (p. 666)
• 14.10 Epilogue and References (p. 673)
• 14.11 Exercises (p. 676)
• 15 An Introduction to LISP (p. 679)
• 15.0 Introduction (p. 679)
• 15.1 LISP: A Brief Overview (p. 680)
• 15.2 Search in LISP: A Functional Approach to the Farmer, Wolf, Goat, and Cabbage Problem (p. 702)
• 15.3 Higher-Order Functions and Procedural Abstraction (p. 707)
• 15.4 Search Strategies in LISP (p. 711)
• 15.5 Pattern Matching in LISP (p. 715)
• 15.6 A Recursive Unification Function (p. 717)
• 15.7 Interpreters and Embedded Languages (p. 721)
• 15.8 Logic Programming in LISP (p. 723)
• 15.9 Streams and Delayed Evaluation (p. 732)
• 15.10 An Expert System Shell in LISP (p. 736)
• 15.11 Semantic Networks and Inheritance in LISP (p. 743)
• 15.12 Object-Oriented Programming Using CLOS (p. 747)
• 15.13 Learning in LISP: The ID3 Algorithm (p. 759)
• 15.14 Epilogue and References (p. 771)
• 15.15 Exercises (p. 772)
• Part VII Epilogue (p. 777)
• 16 Artificial Intelligence as Empirical Enquiry (p. 779)
• 16.0 Introduction (p. 779)
• 16.1 Artificial Intelligence: A Revised Definition (p. 781)
• 16.2 The Science of Intelligent Systems (p. 792)
• 16.3 AI: Current Issues and Future Directions (p. 803)
• 16.4 Epilogue and References (p. 807)
• Bibliography (p. 809)
• Author Index (p. 837)
• Subject Index (p. 843)

Author notes provided by Syndetics