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A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, Principles of Artificial Intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used.Read more...
Electronic reproduction. [S.l.] : HathiTrust Digital Library, 2011. MiAaHDL
Description:
1 online resource (xv, 476 pages) : illustrations
Details:
Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002.
Contents:
Front Cover; Principles of Artificial Intelligence; Copyright Page; Table of Contents; PREFACE; ACKNOWLEDGEMENTS; CREDITS; PROLOGUE; 0.1. Some Applications of Artificial Intelligence; 0.2. Overview; 0.3. Bibliographical and Historical Remarks; CHAPTER 1. PRODUCTION SYSTEMS AND AI; 1.1. Production Systems; 1.2. Specialized Production Systems; 1.3. Comments on the Different Types of Production Systems; 1.4. Bibliographical and Historical Remarks; Exercises; CHAPTER 2. SEARCH STRATEGIES FOR AI PRODUCTION SYSTEMS; 2.1. Backtracking Strategies; 2.2. Graph-search Strategies. 2.3. Uninformed Graph-search Procedures2.4. Heuristic Graph-search Procedures; 2.5. Related Algorithms; 2.6. Measures of Performance; 2.7. Bibliographical and Historical Remarks; Exercises; CHAPTER 3. SEARCH STRATEGIES FOR DECOMPOSABLE PRODUCTION SYSTEMS; 3.1. Searching AND/OR Graphs; 3.2. AO*: A Heuristic Search Procedure for AND/OR Graphs; 3.3. Some Relationships Between Decomposable and Commutative Systems; 3.4. Searching Game Trees; 3.5. Bibliographical and Historical Remarks; Exercises; CHAPTER 4. THE PREDICATE CALCULUS IN AI; 4.1. Informal Introduction to the Predicate Calculus. 4.2. Resolution4.3. The Use of the Predicate Calculus in AI; 4.4. Bibliographical and Historical Remarks; Exercises; CHAPTER 5. RESOLUTION REFUTATION SYSTEMS; 5.1. Production Systems for Resolution Refutations; 5.2. Control Strategies for Resolution Methods; 5.3. Simplification Strategies; 5.4. Extracting Answers From Resolution Refutations; 5.5. Bibliographical and Historical Remarks; Exercises; CHAPTER 6. RULE-BASED DEDUCTION SYSTEMS; 6.1. A Forward Deduction System; 6.2. A Backward Deduction System; 6.3. ""Resolving"" Within AND/OR Graphs; 6.4. Computation Deductions and Program Synthesis. 6.5. A Combination Forward and Backward System6.6. Control Knowledge For Rule-Based Deduction Systems; 6.7. Bibliographical and Historical Remarks; Exercises; CHAPTER 7. BASIC PLAN-GENERATING SYSTEMS; 7.1. Robot Problem Solving; 7.2. A Forward Production System; 7.3. A Representation for Plans; 7.4. A Backward Production System; 7.5. STRIPS; 7.6. Using Deduction Systems to Generate Robot Plans; 7.7. Bibliographical and Historical Remarks; Exercises; CHAPTER 8. ADVANCED PLAN-GENERATING SYSTEMS; 8.1. RSTRIPS; 8.2. DCOMP; 8.3. Amending Plans; 8.4. Hierarchical Planning. 8.5. Bibliographical and Historical RemarksExercises; CHAPTER 9. STRUCTURED OBJECT REPRESENTATIONS; 9.1. From Predicate Calculus to Units; 9.2. A Graphical Representation: Semantic Networks; 9.3. Matching; 9.4. Deductive Operations on Structured Objects ; 9.5 Defaults and Contradictory Information; 9.6. Bibliographical and Historical Remarks; Exercises; PROSPECTUS; 10.1. AI System Architectures; 10.2. Knowledge Acquisition; 10.3. Representational Formalisms; BIBLIOGRAPHY; AUTHOR INDEX; SUBJECT INDEX.
A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, Principles of Artificial Intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used.
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