| Description |
xix, 536 pages : illustrations ; 23 cm |
| Series |
Advanced series on artificial intelligence ; vol. 3 |
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Advanced series on artificial intelligence ; vol. 3
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| Contents |
CONTENTS; PREFACE; CONTRIBUTORS; A New Way to Acquire Knowledge: Knowledge Acquiring Geared to a Problem; ABSTRACT; 1. The knowledge acquisition and developing on resolving problem; 2. The Thinking model of solving a problem in social and economic; 3. The structuring of the expert experience geared to a problem; 4. The structure of the knowledge base system geared to a problem; 5. Acquiring knowledge on problem resolving; 6. Conclusion; References; AN SPN KNOWLEDGE REPRESENTATION SCHEME; ABSTRACT; 1. INTRODUCTION; 2. COMPARISON of KNOWLEDGE REPRESENTATION METHODOLOGIES |
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2 Schema-Based Understanding3 The Deep Structure of Problems; 4 The Deep Structure of Motion Problems; 5 The Construction of the Deep Structure of Motion Problems; 6 The Solution of Motion Problems; 7 Conclusion; References; RESOLVING CONFLICTS IN INHERITANCE REASONING WITH STATISTICAL APPROACH; Abstract; 1 Introduction; 2 Interpretations of Defeasible Assertion; 3 Evidential Probabilities; 4 Model; 5 Detecting the Existence of Rules; 5.1 Semantics of Rules; 5.2 Inheritance Reasoner As a Network; 6 Inferences; 6.1 Specificity; 6.2 Generality; 6.2.1 Immature Class; 6.2.2 Biased Class |
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3. PETRI NETS and PRODUCTION RULES3.1 A PRIMITIVE FOR REPRESENTING KNOWLEDGE ATOMS; 3.2 REPRESENTATION OF KNOWLEDGE; 3.3 EXAMPLE; 3.4 FORWARD AND BACKWARD CHAINING; 4. PETRI NETS and LOGIC; 4.1 EXTENSION OF PRODUCTION RULES TO LOGIC; 4.2 REPRESENTATION OF FOPC: PREDICATE TRANSITION NETS; 5. PETRI NETS and SEMANTIC NETWORKS; 6. EXAMPLES of STRUCTURED COMPUTATION USING PETRI NETS; 6.1 OBJECT RECOGNITION; 6.2 PLANNING; 6.3 PREDICATE CALCULUS CIRCUIT SIMULATION; 7. DISCUSSION; 8. CONCLUSION; REFERENCES; ON THE DEEP STRUCTURES OF WORD PROBLEMS AND THEIR CONSTRUCTION; ABSTRACT; 1 Introduction |
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5.2.6 Complexity of Obtaining the Regions Graph5.2.7 Labeling of the Regions in the Image(Region Labeling, RL); 5.2.8 An Algorithm of Regions Labeling(RL); 5.2.9 Complexity Analysis of Regions Labeling; 5.3 Exponent Regions Labeling (ERL); 5.3.1 Exponent Regions Labeling Algorithm; 5.3.2 Complexity of Exponent Regions Labeling; 5.3.3 Property of Adjacency of Exponent Regions Labeling; 5.3.4 Algorithm for obtaining the Regions Graph Using ERL: (Regions Graph Extraction,RGE); 5.4 Duality between Vertices Graph and Regions Graph; VI. GRAPH REDUCTION ALGORITHMS (GRA); 6.1 Link Weights |
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6.2 Analysis of Threshold |
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7 Algorithm8 Examples; 9 Conclusion; References; INTEGRATING HIGH AND LOW LEVEL COMPUTER VISION FOR SCENE UNDERSTANDING; ABSTRACT; I. INTRODUCTION; II. BACKGROUND; III. PROBLEM STATEMENT; IV. APPROACH; V. GRAPH EXTRACTION ALGORITHM; 5.1 Vertices Graph; 5.1.1 Vertices Graph Extraction (VGE Algorithm); 5.1.2 Complexity of Obtaining the Vertices Graph; 5.2 Regions Graph; 5.2.1 Intersections of Line and Region; 5.2.2 Region Splitting; 5.2.3 Adjacency Properties of Regions Graph; 5.2.4 Link Routing; 5.2.5 Algorithm for Obtaining the Regions Graph: (Regions Graph Extraction, RGE) |
| Summary |
Covering artificial intelligence and automation, these contributions discuss: the evolution of AI tools; an SPN knowledge representation scheme; software engineering using AI; the impact of AI in VLSI design automation; incremental adaptation as a method to improve reactive behaviour; and more |
| Notes |
Description based upon print version of record |
| Bibliography |
Includes bibliographical references |
| Notes |
English |
| Subject |
Artificial intelligence.
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Automation.
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Expert systems (Computer science)
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Knowledge acquisition (Expert systems)
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| Author |
Bourbakis, Nikolaos G.
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| LC no. |
98199776 |
| ISBN |
9810226373 |
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