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.

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.

Research has shown that harassed decision makers, in decision support technologies for real-time and uncertain decision problems, often ignore crucial information, use inefficient strategies, and generate fewer alternatives. Examples of such problems are medical emergencies, traffic flow and military applications. Researchers are limited by a narrow understanding of the most effectual methods to support decision-making in real-time and stressful situations. Intelligent software agents offer one approach to operationalizing a decision making support system in these circumstances by providing an environment that can adapt to speed and change. Agent technology has been used to aid and automate complex problem-solving such as brokering in electronic commerce and financial services. An agent is a software package that carries out tasks for others autonomously, whether the others are human users, business processes, workflows or applications.