Agent-Based Hybrid Intelligent Systems [electronic resource] : An Agent-Based Framework for Complex Problem Solving / by Zili Zhang, Chengqi Zhang.

By: Zhang, Zili [author.]Contributor(s): Zhang, Chengqi [author.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Lecture Notes in Computer Science: 2938Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2004Description: XV, 194 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540246237Subject(s): Computer science | Software engineering | Database management | Artificial intelligence | Information systems | Computer Science | Artificial Intelligence (incl. Robotics) | Software Engineering | Computation by Abstract Devices | Database Management | Computer Appl. in Administrative Data ProcessingAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 LOC classification: Q334-342TJ210.2-211.495Online resources: Click here to access online
Contents:
Fundamentals of Hybrid Intelligent Systems and Agents -- 1 Introduction -- 2 Basics of Hybrid Intelligent Systems -- 3 Basics of Agents and Multi-agent Systems -- Methodology and Framework -- 4 Agent-Oriented Methodologies -- 5 Agent-Based Framework for Hybrid Intelligent Systems -- 6 Matchmaking in Middle Agents -- Application Systems -- 7 Agent-Based Hybrid Intelligent System for Financial Investment Planning -- 8 Agent-Based Hybrid Intelligent System for Data Mining -- Concluding Remarks -- 9 The Less the More -- Appendix: Sample Source Codes of the Agent-Based Financial Planning System -- References.
In: Springer eBooksSummary: 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.
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Fundamentals of Hybrid Intelligent Systems and Agents -- 1 Introduction -- 2 Basics of Hybrid Intelligent Systems -- 3 Basics of Agents and Multi-agent Systems -- Methodology and Framework -- 4 Agent-Oriented Methodologies -- 5 Agent-Based Framework for Hybrid Intelligent Systems -- 6 Matchmaking in Middle Agents -- Application Systems -- 7 Agent-Based Hybrid Intelligent System for Financial Investment Planning -- 8 Agent-Based Hybrid Intelligent System for Data Mining -- Concluding Remarks -- 9 The Less the More -- Appendix: Sample Source Codes of the Agent-Based Financial Planning System -- References.

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.

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