A Practical Framework for Agent-Based Hybrid Intelligent Systems

The design and development of hybrid intelligent systems are difficult because there are many interactions between various components. Existing software development techniques cannot manage those complex interactions efficiently as those interactions may occur at unpredictable times, for unpredictable reasons, between unpredictable components. In this paper, we contribute a multi-agent framework to organize those components and interactions. The framework consists of user interface, decision making, knowledge discovering, information management facilitators, and distributed heterogeneous data resources. We employ the middle agent concept to match task requesters with specific intelligent agents. We demonstrate the potentials of the framework by case study and present theoretical and empirical evidence that our framework is available and robust.