Quotient Space Based Problem Solving

  • ID: 2690449
  • Book
  • 396 Pages
  • Elsevier Science and Technology
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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.
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Chapter 1 Problem Representation

Chapter 2 Hierarchy and Multi-granular Computing

Chapter 3 Information Synthesis in Multi-granular Computing

Chapter 4 Reasoning in Multi-granular Computing

Chapter 5 Automatic Spatial Planning

Chapter 6 Statistical Heuristic Search

Chapter 7 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
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Zhang, Ling
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.
Zhang, Bo
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.
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