Agent-Based Hybrid Intelligent Systems

An Agent-Based Framework for Complex Problem Solving

  • Zili Zhang
  • Chengqi Zhang

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

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 2938)

Table of contents

  1. Front Matter
  2. Fundamentals of Hybrid Intelligent Systems and Agents

    1. Front Matter
      Pages 1-1
    2. Zili Zhang, Chengqi Zhang
      Pages 3-11
    3. Zili Zhang, Chengqi Zhang
      Pages 13-28
    4. Zili Zhang, Chengqi Zhang
      Pages 29-39
  3. Methodology and Framework

    1. Front Matter
      Pages 41-41
    2. Zili Zhang, Chengqi Zhang
      Pages 43-55
    3. Zili Zhang, Chengqi Zhang
      Pages 57-64
    4. Zili Zhang, Chengqi Zhang
      Pages 65-90
  4. Application Systems

    1. Front Matter
      Pages 91-91
    2. Zili Zhang, Chengqi Zhang
      Pages 127-142
  5. Concluding Remarks

    1. Front Matter
      Pages 143-143
    2. Zili Zhang, Chengqi Zhang
      Pages 145-147
    3. Zili Zhang, Chengqi Zhang
      Pages 183-191
  6. Back Matter

About this book

Introduction

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

  • Zili Zhang
    • 1
  • Chengqi Zhang
    • 2
  1. 1.School of Engineering and Information TechnologyDeakin UniversityGeelongAustralia
  2. 2.Faculty of Engineering and Information Technology, Centre for Quantum Computation and Intelligent Systems, and Australian ACS National Committee for Artificial IntelligenceUniversity of TechnologySydneyAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/b95170
  • Copyright Information Springer-Verlag Berlin Heidelberg 2004
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-20908-9
  • Online ISBN 978-3-540-24623-7
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book