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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