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| 005 | 19980603113140.0 | |
| 008 | 860211s1987 mauaf b 001 0 eng | |
| 010 | ▼a 86003557 | |
| 020 | ▼a 0201120011 : ▼c $31.95 | |
| 040 | ▼a DLC ▼c DLC | |
| 049 | 1 | ▼l 421111928 ▼f 과개 ▼l 421111929 ▼f 과개 |
| 050 | 0 0 | ▼a Q335 ▼b .F57 1987 |
| 082 | 0 0 | ▼a 006.3 ▼2 19 |
| 090 | ▼a 006.3 ▼b F529i | |
| 100 | 1 | ▼a Fischler, Martin A. |
| 245 | 1 0 | ▼a Intelligence : ▼b the eye, the brain, and the computer / ▼c Martin A. Fischler, Oscar Firschein. |
| 260 | ▼a Reading, Mass. : ▼b Addison-Wesley, ▼c c1987. | |
| 300 | ▼a xiv, 331 p., [4] p. of plates : ▼b ill. (some col.) ; ▼c 25 cm. | |
| 500 | ▼a Includes index. | |
| 504 | ▼a Bibliography: p. 311-323. | |
| 650 | 0 | ▼a Artificial intelligence. |
| 650 | 0 | ▼a Machine learning. |
| 650 | 0 | ▼a Cognition. |
| 650 | 0 | ▼a Perception. |
| 700 | 1 0 | ▼a Firschein, Oscar. |
Holdings Information
| No. | Location | Call Number | Accession No. | Availability | Due Date | Make a Reservation | Service |
|---|---|---|---|---|---|---|---|
| No. 1 | Location Science & Engineering Library/Sci-Info(Stacks2)/ | Call Number 006.3 F529i | Accession No. 421111928 (2회 대출) | Availability Available | Due Date | Make a Reservation | Service |
| No. 2 | Location Science & Engineering Library/Sci-Info(Stacks2)/ | Call Number 006.3 F529i | Accession No. 421111929 (4회 대출) | Availability Available | Due Date | Make a Reservation | Service |
Contents information
Book Introduction
This book treats the question of how far we have come in understanding intelligence and in duplicating it mechanically. The major facets of intelligence--reasoning, vision, language and learning are discussed as an approach to contrasting biological intelligence with current computer realizations.
Information Provided By: :
Author Introduction
앤 마틴(지은이)
<해티의 지난 여름>
Oscar Firschein(지은이)
Fischler A. Martin(지은이)
Table of Contents
CONTENTS Part One Foundations = 1 1. Intelligence = 3 What is Intelligence? = 3 Theories of Intelligence = 4 Theories of Mind = 8 How Can Intelligence Be Measurd or Evaluated? = 10 Assessing Human Intelligence = 10 Assessing Machine Intelligence = 12 Is Man The Only Intelligent Animal? = 12 The Machinery of Intelligence Reliance on Paradigms = 13 Two Basic Paradigms = 13 Artificial Intelligence(AI) = 15 The Mechanization of Thought = 15 The Computer and the Two Paradigms = 18 How Can We Distinguish Between Mechanical and Intelligent Behavior? = 18 The Role of Representation in Intelligent Behavior = 20 SUMMARY AND DISCUSSION = 20 2. The Brain and The Computer = 23 The Human Brain = 24 Evolution of the Brain = 24 Architecture of the Brain = 30 The Computer = 39 The Nature of Computer Programs and Algorithms = 40 The Universal Turing Machine = 43 Limitations on the Computational Ability of a Logical Device = 43 The G$$\ddot o$$ del Incompleteness Theorem = 43 Unsolvability by Machine = 45 Implications of G$$\ddot o$$ del's Theorem = 46 Computational Complexity-the Existence of Solvable but Intrinsically Difficult Problems = 47 Limitations on the Computational Ability of a Physical Device = 49 Reliable Computation With Unreliable Components = 51 DISCUSSION = 55 Appendixes = 58 2-1 The Nerve Cell and Nervous System Organization = 58 2-2 The Digital Computer = 61 3. The Representation of Knowledge = 63 Representation : Concepts = 64 Form vs. Content of Knowledge = 64 Representing Knowledge = 65 The Relation Between a Representation and Things Represented = 66 Role of Representation = 67 Representations Employed in Human Thinking = 67 The Use of Models and Representations = 68 The Use of "Visual" Representations = 69 Effectiveness of a Representation = 69 R$$\acute e$$ presentations Employed in Artificial Intelligence = 71 Feature Space (or Decision Space) = 74 Decision Tree / Gme Tree = 75 Isomorphic／Iconic／Analogical Representations = 77 DISCUSSION = 80 Part Two Cognition = 81 4. Reasoning and Problem Solving = 83 Human Reasoning = 84 Human Logical Reasoning = 85 Human Probabilistic Reasoning = 86 Formal Reasoning and Problem Solving = 87 Requirements for a Problem Solver = 87 Categories of Reasoning = 88 The Deductive Logic Formalism = 90 Propositional Calculus = 90 Propositional Resolution = 91 Predicates = 93 Quandfiers = 93 Semantics = 93 Computational Issues = 94 Nonstandard Logics = 95 Inductive Reasoning = 96 Measures of Belief = 97 Bayesian Reasoning = 98 Belief Functions = 100 Representing a Problem in a Probabilistic Formalism = 103 Comments Concerning the Probabilistic Formlism = 103 Additional Formalisms for Reasoning = 106 Algebraic / Mathematical Systems = 106 Heuristic Search = 106 Programming Systems that Facilitate Reasoning and Problem Solving = 108 Common-Sense Reasoning = 109 Problem Solving and Theorem Proving = 110 Representing the Problem = 111 The Predicate Calculus Representation for the Monkey／Banana(M／B) Problem = 112 PROLOG Representation of the M / B Problem = 113 Production Rule(OPS-5) Representation for the M / B Problem = 113 General Problem Solver Representation for the M / B Problem = 114 Formalisms or Reasoning Systems? = 115 Relating Reasoning Formalisms to the Real World = 115 DISCUSSION = 116 Appendixes = 117 4-1 AI Programming Languages = 117 4-2 The Monkey / Bananas Problem = 122 5. Learning = 129 Human and Animal Learning = 130 Types of Animal Learning = 131 Piaget's Theory of Human Intellectual Development = 132 Similarity = 135 Similarity Based on Exact Match = 136 Similarity Based on Approximate Match = 137 Learning = 137 Model Instantiation : Parameter Learning = 138 Model Construction : Description Models = 143 Concept Learning = 148 DISCUSSION = 151 Appendix = 152 5-1 Parameter Learning for an Implicit Model = 152 6. Language and Communication = 157 Language in Animals and Man = 158 Brain Structures Associated with Language Production and Understanding = 159 Human Acquisition of Language = 161 Animal Acquisition of Language = 164 Language and Thought = 165 Communication = 167 The Mechanics of Communication = 167 Vocabulary of Communication = 168 Understanding Language = 169 Machine Understanding of Language = 171 Faking Understanding = 171 What Does it Mean for a Computer to Understand? = 171 The Study of Language = 173 DISCUSSION = 185 Appendix = 186 6-1 Representing Passing Algorithms = 186 Human Experts = 190 Production Systems = 191 Control Structures Used in Production Systems = 192 Production Systems in Psychological Modeling = 195 Production Rule-Type Expert Systems = 97 Plausible Reasoning in Expert Systems = 198 Basic AI Issues = 200 DISCUSSION = 202 Appendix = 202 7-1 PROSPECTOR Procedure for Hypothesis Updating = 202 Part Three Perception(Vision) = 205 8. Vision = 207 The Nature of Organic Vision = 207 The Evolution and Physiology of Organic Vision = 209 Seeing and the Evolution of Intelligence = 209 Evolution and Physiology of the Organic Eye = 211 Eye and Brain = 213 The Psychology of Vision = 220 Perceiving the Visual World : Recognizing Patterns = 220 Perceptual Organization = 224 Visual Illusions = 226 Visual Thinking, Visual Memory, and Cultural Factors = 229 DISCUSSION = 232 Appendixes = 233 8-1 Color Vision and Light = 233 8-2 Stereo Depth Perception and the Structure of the Human Visual Cortex = 236 9. Computational Vision = 239 Signals-to-Symbols Paradigm = 241 Low Level Scene Analysis(LLSA) = 242 Image Acquisition(Scanning and Quantizing) = 243 Image Preprocessing(Thresholding and Smoothing) = 245 Detection of Local Discontinuities and Homogeneities(Edges, Texture, Color) = 248 Local Scene Geometry from a Single Image(Shape from Shading and Texture) = 256 Local Scene Geometry from Multiple Images(Stereo and Optic Flow) = 259 Intermediate Level Scene Analysis(ILSA) = 262 Image / Scene Partitioning = 264 Edge Linking and Deriving a Line Sketch = 269 Recovering Three-Dimensional Scene Geometry from a Line Drawing = 272 Image Matching = 276 Object Labeling = 278 Model Selection and Instantiation = 279 High Level Scene Analysis(HLSA) = 281 Image / Scene Description = 281 Knowledge Representation = 283 The Problem of High-Level Scene Analysis = 285 Reasoning About a Simple Scene = 285 DISCUSSION = 286 A Basic Concern About Signals-to-Symbols = 287 Necessary Attributes of a Machine Vision System = 288 Summary = 289 Appendixes = 289 9-1 Mathematical Techniques for Information Integration = 289 9-2 A Path-finding Algorithm = 297 9-3 Relational(Rubber Sheet) Image Matching = 299 Epilogue = 301 Bibliography = 311 Index = 325