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Book Cover
Book
Author Bourbakis, Nikolaos G.

Title Artificial intelligence methods and applications / edited by Nikolaos G. Bourbakis
Published Singapore : World Scientific, [1992]
©1992

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Location Call no. Vol. Availability
 W'PONDS  006.3 Bou/Aim  TEMPORARILY UNAVAILABLE
Description xxiv, 705 pages : illustrations ; 22 cm
Series Advanced series on artificial intelligence ; vol. 1
Advanced series on artificial intelligence ; vol. 1
Contents CONTENTS; INTRODUCTION TO ADVANCED SERIES ON ARTIFICIAL INTELLIGENCE; A BRIEF INTRODUCTION OF AI; FOREWORD TO THE SERIES; PROLOGUE; CONTRIBUTORS; SECTION 1 LOGIC; FUNDAMENTAL METHODS FOR HORN LOGIC AND ARTIFICIAL INTELLIGENCE APPLICATIONS; 1 INTRODUCTION; 1.1 The Scope and Motivation of this Chapter; 1.2 The Character of Rewrite Methods; 1.3 Some History and Relations to Other Approaches; 1.4 Layout of the Chapter; 2 HORN LOGIC; 2.1 The Subsumption Lattice of First-OrderTerms; 2.2 Atoms and Horn Formulas; 3 REWRITING METHODS FOR HORN LOGIC; 3.1 Overview of Rewriting Concepts
2. OVERVIEW OF THE APPROACH: TWO EXAMPLES2.1. Example 1: Block World; 2.2. Example 2: Elementary Number Theory; 3. THE BASIC NOTIONS; 3.1. Terms, Atoms and Substitutions; 3.2. Some Horn Logic; 4. A LOGICAL BASIS FOR LEARNING; 4.1. Defining Learnable Concepts; 4.2. Getting Learnable Concepts: two theorems; 4.3. A General Procedure for Learning; 5. CONCLUSION; REFERENCES; CHAPTER 5 Using Genetic Algorithms for Supervised Concept Learning; 1. Introduction; 2. Supervised Concept Learning Problems; 3. Genetic Algorithms and Concept Learning; 3.1. Representing the Search Space
2.1. Basic Definitions2.2. The GA Recombination Operators; 2.3. The Schema Theorem; 3. Permutation Problems; 4. Analysis of Order Crossover; 5. Experimental Results; 6. Conclusions; References; Appendix A. Problem Data; Appendix B. Crossover Rules; B.1. Interleaving Crossover Using a Fixed Number of Crossover Sites; B.2. Interleaving Using a Random Number of Crossing Sites; B.3. Permutation Crossover Rules; SECTION 3 LEARNING; CHAPTER 4 A LOGICAL BASIS FOR LEARNING; 1. INTRODUCTION; 1.1. The Problem of Concept Learning; 1.2. The Aims of the Present Chapter; 1.3. Layout of the Chapter
3.2 Rewriting for Horn Logic4 APPLICATIONS; 4.1 Automated Theorem Proving; 4.2 Learning; 43 Program Synthesis; 4.4 Diagnosis; 5 BIBLIOGRAPHY; SECTION 2 SEARCHING; CHAPTER 2 On Optimizing a Search Problem1; 1 Introduction; 2 Basic Search Techniques; 3 Optimization for a Search Problem; 3.1 The Optimization Problem; 3.2 Local Optimization; 3.3 Optimizing a Search Problem; 4 The Satisfiability (SAT) Problem; 5 A Discrete Local Search Algorithm for the Satisfiability Problem; 6 Average Time Analysis; 7 UniSAT: A Universal SAT Problem Model
3.2. Defining Fixed-length Classifier Rules
8 A Quantitative Local Search Algorithm for the Satisfiability Problem9 Experimental Results; 9.1 Performance of the SAT1 algorithm; 9.2 Performance of the SAT14.2 algorithm; 10 Conclusion; Acknowledgements; APPENDIX A: The Average Time Complexities of Several SAT1 Algorithms; A.1 Preliminaries; A.2 The Average Time Complexity of the SAT1.1 Algorithm; A.3 The Average Time Complexity of the SAT1.2 Algorithm; A.4 The Average Time Complexity of the SAT1.3 Algorithm; References; CHAPTER 3 APPLICATION OF GENETIC ALGORITHMS TO PERMUTATION PROBLEMS; 1. Introduction; 2. Background
Notes Description based upon print version of record
Bibliography Includes bibliographical references (pages 675-705)
Notes English
Subject Artificial intelligence.
Artificial intelligence.
Self-organizing systems.
Author Bourbakis, Nikolaos G.
LC no. 92041029
ISBN 9810210574
Other Titles Advanced Series On Artificial Intelligence vol. 1
Advanced Series On Artificial Intelligence, Volume 1
Advanced Series on Artificial Intelligence