Secure CheckoutPersonal information is secured with SSL technology.
Free ShippingFree global shipping
No minimum order.
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
Quotient Space Based Problem Solving is designed for graduate students, research fellows and technicians in Computer Science, especially Artificial Intelligence, and those concerned with computerized problem solving.
Chapter 1. Problem Representations
1.1 Problem Solving
1.2 World Representations at Different Granularities
1.3 The Acquisition of Different Grain-Size Worlds
1.4 The Relation Among Different Grain Size Worlds
1.5 Property-Preserving Ability
1.6 Selection and Adjustment of Grain-Sizes
Chapter 2. Hierarchy and Multi-Granular Computing
2.1 The Hierarchical Model
2.2 The Estimation of Computational Complexity
2.3 The Extraction of Information on Coarsely Granular Levels
2.4 Fuzzy Equivalence Relation and Hierarchy
2.5 The Applications of Quotient Space Theory
Chapter 3. Information Synthesis in Multi-Granular Computing
3.2 The Mathematical Model of Information Synthesis
3.3 The Synthesis of Domains
3.4 The Synthesis of Topologic Structures
3.5 The Synthesis of Semi-Order Structures
3.6 The Synthesis of Attribute Functions
Chapter 4. Reasoning in Multi-Granular Worlds
4.1 Reasoning Models
4.2 The Relation Between Uncertainty and Granularity
4.3 Reasoning (Inference) Networks (1)
4.4 Reasoning Networks (2)
4.5 Operations and Quotient Structures
4.6 Qualitative Reasoning
4.7 Fuzzy Reasoning Based on Quotient Space Structures
Chapter 5. Automatic Spatial Planning
5.1 Automatic Generation of Assembly Sequences
5.2 The Geometrical Methods of Motion Planning
5.3 The Topological Model of Motion Planning
5.4 Dimension Reduction Method
Chapter 6. Statistical Heuristic Search
6.1 Statistical Heuristic Search
6.2 The Computational Complexity
6.3 The Discussion of Statistical Heuristic Search
6.4 The Comparison between Statistical Heuristic Search and A∗ Algorithm
6.5 SA in Graph Search
6.6 Statistical Inference and Hierarchical Structure
Chapter 7. The Expansion of Quotient Space Theory
7.1 Quotient Space Theory in System Analysis
7.2 Quotient Space Approximation and Second-Generation Wavelets
7.3 Fractal Geometry and Quotient Space Analysis
7.4 The Expansion of Quotient Space Theory
Addenda A. Some Concepts and Properties of Point Set Topology
Addenda B. Some Concepts and Properties of Integral and Statistical Inference
- No. of pages:
- © Morgan Kaufmann 2014
- 13th February 2014
- Morgan Kaufmann
- Hardcover ISBN:
- eBook ISBN:
Professor Ling Zhang is currently with the Department of Computer Science at Anhui University in Hefei, China. His main interests are artificial intelligence, machine learning, neural networks, genetic algorithms and computational intelligence.
Professor, Department of Computer Science at Anhui University in Hefei, China
Professor Bo Zhang is currently with the Computer Science and Technology Department at Tsinghua University in Beijing, China, He is a Fellow of Chinese Academy of Sciences. His main research interests include artificial intelligence, robotics, intelligent control and pattern recognition. He has published over 150 papers and 3 monographs in these fields.
Professor, Computer Science and Technology Department at Tsinghua University in Beijing, China
"The entire book is devoted to formalize and automate...a theory of granular computing, which is essentially based on quotient spaces." --Zentralblatt MATH
"... aimed primarily at graduate students (and academicians) with strong mathematical maturity and an interest in mathematical modeling in the fields around artificial intelligence (AI)." --Computing Reviews, November 2014
Elsevier.com visitor survey
We are always looking for ways to improve customer experience on Elsevier.com.
We would like to ask you for a moment of your time to fill in a short questionnaire, at the end of your visit.
If you decide to participate, a new browser tab will open so you can complete the survey after you have completed your visit to this website.
Thanks in advance for your time.