Theory and Applications of Problem Solving
Published on 21. July 1992
Book
Hardback
244 pages
978-0-444-89565-3 (ISBN)
Description
Research results obtained by the authors in recent years form the basis of this volume. The motivation behind this research is the authors' belief that more human-like characteristics in problem solving should be involved in a formal representation in order to achieve better performance for computer-based problem solvers. A large part of the material is presented in the language of mathematics, including elementary topology, set theory and statistics. To help the less mathematically-trained readers follow the basic concepts and techniques, basic definitions and theorems are presented before the discussions. A conscious effort is made to introduce simple examples and applications in each topic. This book is designed for graduate students, research fellows and technicians in Computer Science, especially Artificial Intelligence, and also those concerned with computerised problem solving.
Research results obtained by the authors in recent years form the basis of this volume. The motivation behind this research is the authors' belief that more human-like characteristics in problem solving should be involved in a formal representation in order to achieve better performance for computer-based problem solvers. A large part of the material is presented in the language of mathematics, including elementary topology, set theory and statistics. To help the less mathematically-trained readers follow the basic concepts and techniques, basic definitions and theorems are presented before the discussions. A conscious effort is made to introduce simple examples and applications in each topic. This book is designed for graduate students, research fellows and technicians in Computer Science, especially Artificial Intelligence, and also those concerned with computerised problem solving.
Research results obtained by the authors in recent years form the basis of this volume. The motivation behind this research is the authors' belief that more human-like characteristics in problem solving should be involved in a formal representation in order to achieve better performance for computer-based problem solvers. A large part of the material is presented in the language of mathematics, including elementary topology, set theory and statistics. To help the less mathematically-trained readers follow the basic concepts and techniques, basic definitions and theorems are presented before the discussions. A conscious effort is made to introduce simple examples and applications in each topic. This book is designed for graduate students, research fellows and technicians in Computer Science, especially Artificial Intelligence, and also those concerned with computerised problem solving.
More details
Series
Language
English
Place of publication
United States
Publishing group
Elsevier Science & Technology
Target group
College/higher education
Professional and scholarly
Illustrations
Illustrations
ISBN-13
978-0-444-89565-3 (9780444895653)
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Schweitzer Classification
Persons
Content
Problem Representations. Problem Solving. World Representations at Different Granularities. The Relation Between Different Grain Size Worlds. Semi-Order Lattice. Attribute-Preserving. Selection and Adjustments of Grain-Sizes. Mergence. Decomposition. Conclusions. Hierarchy. The Model of Hierarchy. The Estimation of Computational Complexity. The Assumptions. The Estimation of The Complexity Under Deterministic Model. The Estimation of The Complexity Under Probabilistic Model. Successive Operation in Hierarchy. The Extraction of Information on High Abstraction Level. Examples. Constructing [f] Under Unstructured Domain. Constructing [f] Under Structured Domain. Conclusions. Fuzzy Equivalence Relation and Hierarchy. Fuzzy Quotient Structure. Cluster. Combination. Introduction. The Mathematical Model of Combination. The Combination of Domains. The Combination of Topologic Structure. The Combination of Semi-Order Structure. The Graphical Constructing Method of Quotient Semi-Order. The Combination of Semi-Order Structure. The Combination of Attribute Functions. The Combination Principle of Attribute Functions. Examples. Conclusions. Reasoning Model. Reasoning Models. Inference Network (I). Projection. Combination. Inference Network (II). Modeling. The Projection of AND/OR Relations. The Combination of AND/OR Relations. Operations and Quotient Structures. The Existence of Quotient Operation. Construction of Quotient Operations. The Approximation of Quotient Operation. Constraints and Quotient Constraints. Qualitative Reasoning. Qualitative Reasoning Model. Examples. The Procedure of Qualitative Reasoning. Planning. Automatic Generation of Assembly Sequences. Algorithms. Examples. Computational Complexity. Conclusions. Geometrical Methods of Motion Planning. Configuration Space Representation. Finding Collision-Free Paths. Topological Model of Motion Planning. Rotation Mapping Graph (RMG). The Topologic Model of Motion Planning. Homotopically Equivalent Classification. Dimension Reduction Method. Basic Principle. Characteristic Network. Applications. Collision-Free Paths Planning For A Planar Rod. Motion Planning For A Multi-Joint Arm. The Application of The Hierarchical Technique. The Hierarchical Motion Planning of Multi-Joint Arm. Estimation of The Computational Complexity. Statistical Heuristic Search. Statistical Inference Method in Heuristic Search. Heuristic Search. Statistical Inference. Statistical Heuristic Search. The Computational Complexity. The Model of Search Tree. SPA Search. SAA Search. Different Kinds of SA. The Extraction of Global Information. Hypothesis I. The Extraction of Global Statistic. Algorithm SA. The Comparison Between the Statistical Heuristic Search and A*. Comparison With A*. Comparison To Other Weighted Techniques. General Graph Search. Conclusions. References.