Building Agent-Based Hybrid Intelligent Systems

Many complex problems including financial investment planning, foreign exchange trading, knowledge discovery from large/multiple databases require hybrid intelligent systems that integrate many intelligent techniques including expert systems, fuzzy logic, neural networks, and genetic algorithms. However, hybrid intelligent systems are difficult to develop because they have a large number of parts or components that have many interactions. On the other hand, agents offer a new and often more appropriate route to the development of complex systems, especially in open and dynamic environments. In this paper, it is argued that agent technology is well snited for constructing hybrid intelligent systems (especially loosely coupled hybrid intelligent systems) through a successful case study. A great number of heterogeneous computing techniques/packages are easily integlated into the experimental system under a unifying agent framework, which implies that agent technology can greatly facilitate the construction of hybrid intelligent systems.