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Artificial Intelligence
- 1st Edition - May 10, 2014
- Author: Earl B. Hunt
- Editors: Edward C. Carterette, Morton P. Friedman
- Language: English
- Paperback ISBN:9 7 8 - 1 - 4 8 3 2 - 4 0 8 0 - 0
- eBook ISBN:9 7 8 - 1 - 4 8 3 2 - 6 3 1 7 - 5
Artificial Intelligence provides information pertinent to the fundamental aspects of artificial intelligence. This book presents the basic mathematical and computational approaches… Read more
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Request a sales quoteArtificial Intelligence provides information pertinent to the fundamental aspects of artificial intelligence. This book presents the basic mathematical and computational approaches to problems in the artificial intelligence field. Organized into four parts encompassing 16 chapters, this book begins with an overview of the various fields of artificial intelligence. This text then attempts to connect artificial intelligence problems to some of the notions of computability and abstract computing devices. Other chapters consider the general notion of computability, with focus on the interaction between computability theory and artificial intelligence. This book discusses as well the concepts of pattern recognition, problem solving, and machine comprehension. The final chapter deals with the study of machine comprehension and reviews the fundamental mathematical and computing techniques underlying artificial intelligence research. This book is a valuable resource for seniors and graduate students in any of the computer-related sciences, or in experimental psychology. Psychologists, general systems theorists, and scientists will also find this book useful.
PrefaceAcknowledgmentsI Introduction Chapter I The Scope of Artificial Intelligence 1.0 Is There Such a Thing? 1.1 Problem Solving 1.2 Pattern Recognition 1.3 Game Playing and Decision Making 1.4 Natural Language and Machine Comprehension 1.5 Self-Organizing Systems 1.6 RobotologyChapter II Programming, Program Structure, and Computability 2.0 The Relevance of Computability 2.1 Computations on Strings 2.2 Formal Grammars 2.3 Turing Machines 2.4 Linear Bounded Automata and Type 1 Languages 2.5 Pushdown Automata and Type 2 Languages 2.6 Finite Automata and Regular (Type 3) Languages 2.7 Summary and Comments on PracticalityII Pattern Recognition Chapter III General Considerations in Pattern Recognition 3.0 Classification 3.1 Categorizing Pattern-Recognition Problems 3.2 Historical Perspective and Current Issues Chapter IV Pattern Classification and Recognition Methods Based on Euclidean Description Spaces 4.0 General 4.1 Bayesian Procedures in Pattern Recognition 4.2 Classic Statistical Approach to Pattern Recognition and Classification 4.3 Classification Based on Proximity of Descriptions 4.4 Learning Algorithms 4.5 Clustering Chapter V Non-Euclidean Parallel Procedures: The Perceptron 5.0 Introduction and Historical Comment 5.1 Terminology 5.2 Basic Theorems for Order-Limited Perceptrons 5.3 Substantive Theorems for Order-Limited Perceptrons 5.4 Capabilities of Diameter-Limited Perceptrons 5.5 The Importance of Perceptron Analysis Chapter VI Sequential Pattern Recognition 6.0 Sequential Classification 6.1 Definitions and Notation 6.2 Bayesian Decision Procedures 6.3 Bayesian Optimal Classification Procedures Based on Dynamic Programming 6.4 Approximations Based on Limited Look Ahead Algorithms 6.5 Convergence in Sequential Pattern Recognition Chapter VII Grammatical Pattern Classification 7.0 The Linguistic Approach to Pattern Analysis 7.1 The Grammatical Inference Problem 7.2 Grammatical Analysis Applied to Two-Dimensional Images Chapter VIII Feature Extraction 8.0 General 8.1 Formalization of the Factor-Analytic Approach 8.2 Formalization of the Binary Measurement Case 8.3 Constructive Heuristics for Feature Detection 8.4 An Experimental Study of Feature Generation in Pattern Recognition 8.5 On Being CleverIII Theorem Proving and Problem Solving Chapter IX Computer Manipulable Representations in Problem Solving 9.0 The Use of Representations 9.1 A Typology of Representations 9.2 Combining Representations Chapter X Graphic Representations in Problem Solving 10.0 Basic Concepts and Definitions 10.1 Algorithms for Finding a Minimal Path to a Single Goal Node 10.2 An "Optimal" Ordered Search Algorithm 10.3 Tree Graphs and Their Use Chapter XI Heuristic Problem-Solving Programs 11.0 General Comments 11.1 Terminology 11.2 The General Problem Solver (GPS) 11.3 The Fortran Deductive System-Automatic Generation of Operator-Difference Tables 11.4 Planning Chapter XII Theorem Proving 12.0 Theorem Proving Based on Herbrand Proof Procedures 12.1 The Resolution Principle 12.2 Simple Refinement Strategies 12.3 Ancestory Strategies 12.4 Syntactic Strategies 12.5 Semantic Strategies 12.6 Heuristics 12.7 Quantification 12.9 Problems and Future DevelopmentIV Comprehension Chapter XIII Computer Perception 13.0 The Problem of Perception 13.1 Vision 13.2 Perception of Speech by Computer Chapter XIV Question Answering 14.0 The Problem 14.1 Data Structures 14.2 Deductive Inference in Information Retrieval 14.3 Comprehension without logic Chapter XV Comprehension of Natural Language 15.0 The Problem 15.1 Natural Language: The Mathematical Model 15.2 The Psychological Model Chapter XVI Review and Prospectus 16.0 Things Done and Undone 16.1 Some Problems of Philosophy 16.2 A General Theory of ThoughtReferencesAuthor IndexSubject Index
- No. of pages: 480
- Language: English
- Edition: 1
- Published: May 10, 2014
- Imprint: Academic Press
- Paperback ISBN: 9781483240800
- eBook ISBN: 9781483263175