| Many complex problems, such as financial investment planning, involve many different components or sub-tasks, each of which requires different types of processing. To solve such complex problems, a great diversity of intelligent techniques, including traditional hard computing techniques (e.g., expert systems) and soft computing techniques (e.g., fuzzy logic, neural networks, and genetic algorithms), are required. These techniques are complementary rather than competitive, and thus must be used in combination and not exclusively. This results in systems called hybrid intelligent systems. In other words, hybrid solutions are crucial for complex problem solving and decision-making. However, the design and development of hybrid intelligent systems is difficult because they have a large number of parts or components that have many interactions. Existing software development techniques cannot manage these complex interactions efficiently as these interactions may occur at unpredictable times, for unpredictable reasons, and between unpredictable components.
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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