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  • © 2004

Agent-Based Hybrid Intelligent Systems

An Agent-Based Framework for Complex Problem Solving

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 2938)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

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Table of contents (11 chapters)

  1. Front Matter

  2. Fundamentals of Hybrid Intelligent Systems and Agents

    1. Front Matter

      Pages 1-1
    2. 1 Introduction

      • Zili Zhang, Chengqi Zhang
      Pages 3-11
    3. 2 Basics of Hybrid Intelligent Systems

      • Zili Zhang, Chengqi Zhang
      Pages 13-28
    4. 3 Basics of Agents and Multi-agent Systems

      • Zili Zhang, Chengqi Zhang
      Pages 29-39
  3. Methodology and Framework

    1. Front Matter

      Pages 41-41
    2. 4 Agent-Oriented Methodologies

      • Zili Zhang, Chengqi Zhang
      Pages 43-55
    3. 5 Agent-Based Framework for Hybrid Intelligent Systems

      • Zili Zhang, Chengqi Zhang
      Pages 57-64
    4. 6 Matchmaking in Middle Agents

      • Zili Zhang, Chengqi Zhang
      Pages 65-90
  4. Application Systems

    1. Front Matter

      Pages 91-91
    2. 8 Agent-Based Hybrid Intelligent System for Data Mining

      • Zili Zhang, Chengqi Zhang
      Pages 127-142
  5. Concluding Remarks

    1. Front Matter

      Pages 143-143
    2. 9 The Less the More

      • Zili Zhang, Chengqi Zhang
      Pages 145-147
    3. References

      • Zili Zhang, Chengqi Zhang
      Pages 183-191
  6. Back Matter

About this book

Solving complex problems in real-world contexts, such as financial investment planning or mining large data collections, involves many different sub-tasks, each of which requires different techniques. To deal with such problems, a great diversity of intelligent techniques are available, including traditional techniques like expert systems approaches and soft computing techniques like fuzzy logic, neural networks, or genetic algorithms. These techniques are complementary approaches to intelligent information processing rather than competing ones, and thus better results in problem solving are achieved when these techniques are combined in hybrid intelligent systems. Multi-Agent Systems are ideally suited to model the manifold interactions among the many different components of hybrid intelligent systems.

This book introduces agent-based hybrid intelligent systems and presents a framework and methodology allowing for the development of such systems for real-world applications. The authors focus on applications in financial investment planning and data mining.

Keywords

  • agent technology
  • agent-based systems
  • classification
  • clustering
  • computational finance
  • data mining
  • decision making
  • expert systems
  • fuzzy logic
  • genetic algorithms
  • hybrid intelligent systems
  • intelligent agents
  • intelligent systems
  • knowledge processing
  • multi-agent systems
  • neural computing

Authors and Affiliations

  • School of Engineering and Information Technology, Deakin University, Geelong, Australia

    Zili Zhang

  • Faculty of Engineering and Information Technology, Centre for Quantum Computation and Intelligent Systems, and Australian ACS National Committee for Artificial Intelligence, University of Technology, Sydney, Australia

    Chengqi Zhang

Bibliographic Information

Buying options

eBook USD 59.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 79.99
Price excludes VAT (Canada)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions