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Quotient Space Based Problem Solving

Book Description

Quotient Space Based Problem Solving provides an in-depth treatment of hierarchical problem solving, computational complexity, and the principles and applications of multi-granular computing, including inference, information fusing, planning, and heuristic search.




          • Explains the theory of hierarchical problem solving, its computational complexity, and discusses the principle and applications of multi-granular computing

            • Describes a human-like, theoretical framework using quotient space theory, that will be of interest to researchers in artificial intelligence.

              • Provides many applications and examples in the engineering and computer science area.

                • Includes complete coverage of planning, heuristic search and coverage of strictly mathematical models.

      Table of Contents

      1. Cover image
      2. Title page
      3. Table of Contents
      4. Copyright
      5. Preface
      6. Chapter 1. Problem Representations
        1. 1.1 Problem Solving
        2. 1.2 World Representations at Different Granularities
        3. 1.3 The Acquisition of Different Grain-Size Worlds
        4. 1.4 The Relation Among Different Grain Size Worlds
        5. 1.5 Property-Preserving Ability
        6. 1.6 Selection and Adjustment of Grain-Sizes
        7. 1.7 Conclusions
      7. Chapter 2. Hierarchy and Multi-Granular Computing
        1. 2.1. The Hierarchical Model
        2. 2.2. The Estimation of Computational Complexity
        3. 2.3. The Extraction of Information on Coarsely Granular Levels
        4. 2.4. Fuzzy Equivalence Relation and Hierarchy
        5. 2.5. The Applications of Quotient Space Theory
        6. 2.6. Conclusions
      8. Chapter 3. Information Synthesis in Multi-Granular Computing
        1. 3.1. Introduction
        2. 3.2. The Mathematical Model of Information Synthesis
        3. 3.3. The Synthesis of Domains
        4. 3.4. The Synthesis of Topologic Structures
        5. 3.5. The Synthesis of Semi-Order Structures
        6. 3.6. The Synthesis of Attribute Functions
      9. Chapter 4. Reasoning in Multi-Granular Worlds
        1. 4.1. Reasoning Models
        2. 4.2. The Relation Between Uncertainty and Granularity
        3. 4.3. Reasoning (Inference) Networks (1)
        4. 4.4. Reasoning Networks (2)
        5. 4.5. Operations and Quotient Structures
        6. 4.6. Qualitative Reasoning
        7. 4.7. Fuzzy Reasoning Based on Quotient Space Structures
      10. Chapter 5. Automatic Spatial Planning
        1. 5.1 Automatic Generation of Assembly Sequences
        2. 5.2 The Geometrical Methods of Motion Planning
        3. 5.3 The Topological Model of Motion Planning
        4. 5.4 Dimension Reduction Method
        5. 5.5 Applications
      11. Chapter 6. Statistical Heuristic Search
        1. 6.1. Statistical Heuristic Search
        2. 6.2. The Computational Complexity
        3. 6.3. The Discussion of Statistical Heuristic Search
        4. 6.4. The Comparison between Statistical Heuristic Search and A∗ Algorithm
        5. 6.5. SA in Graph Search
        6. 6.6. Statistical Inference and Hierarchical Structure
      12. Chapter 7. The Expansion of Quotient Space Theory
        1. 7.1. Quotient Space Theory in System Analysis
        2. 7.2. Quotient Space Approximation and Second-Generation Wavelets
        3. 7.3. Fractal Geometry and Quotient Space Analysis
        4. 7.4. The Expansion of Quotient Space Theory
        5. 7.5. Conclusions
      13. Addenda A. Some Concepts and Properties of Point Set Topology
      14. Addenda B. Some Concepts and Properties of Integral and Statistical Inference
      15. References
      16. Index