Agent-Based Hybrid Intelligent Systems (Paperback)

Zili Zhang

ISBN 10: 3540209085 / ISBN 13: 9783540209089

Verlag: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2004

Beschreibung

Paperback. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Bestandsnummer des Verkäufers 9783540209089

Bibliografische Details

• Titel: Agent-Based Hybrid Intelligent Systems (Paperback)
• Verlag: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin
• Erscheinungsjahr: 2004
• Sprache: Englisch
• ISBN-10: 3540209085
• ISBN-13: 9783540209089
• Einband: Paperback
• Zustand: new

Über diesen Titel

Inhaltsangabe

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.