A causal knowledge-driven inference engine for expert system

A wide variety of knowledge acquisition methods exist for conventional knowledge types such as production rules, semantic knowledge, etc. However, the need for causal knowledge acquisition has not been stressed in the expert systems field. The objectives of this paper are to: suggest a causal knowledge acquisition process; and investigate the causal knowledge-based inference process. FCM (Fuzzy Cognitive Map), a fuzzy signed digraph with causal relationships between concept variables found in a specific application domain, is used for the causal knowledge acquisition. Although FCM has plenty of generic properties for causal knowledge acquisition, it needs some theoretical improvement for acquiring more refined causal knowledge. In this sense, we refine fuzzy implications of FCM by proposing a fuzzy causal relationship and fuzzy partially causal relationship. To test the validity of our proposed approach, we prototype a causal knowledge-driven inference engine named CAKES and then experiment with illustrative examples.