An Agent Based Approach to Expert System Explanation

During the last few years, expert system explanation has become an active research area after recognizing that its role goes beyond expert system verification as assumed by early systems. After examining current systems, it has been found that only a few of these meet the requirements of an end user interested in learning more about the domain being addressed by an expert system and understanding why it has reached a certain conclusion. It was also found that systems that address these requirements, do so at a very high cost, since they embed immense amounts of knowledge in a system without providing any means for accessing this knowledge except by the system for which it was built. The primary goal of this paper is to investigate the use of an agent based approach for the explanation problem, such that knowledge re-usability would be promoted and high quality explanations generated. Through the implementation of an experimental prototype, the approach presented was found to show great promise since it satisfied the addressed explanation goals, achieved knowledge re-usability, and modularity. The devised architecture was also found to be scaleable and open, and to promote parallelism.