A Combined Optimiiation Method to Refine Knowledge Bases of Uncertain Rules

One of the important issues when designing effective expert systems is the validation and refinement of the acquired knowledge bases. The validation and refinement problem becomes more important and more difficult when the knowledge bases of expert systems consist of uncertain rules, e.g., probabilistic rules. In this paper, we first describe one type of inconsistency of knowledge bases called sociopathicity and summarize some results. We then develop the Combined Optimization Method to debug this type of knowledge bases. This method utilizes the static and dynamic information of the rules. ’ Our experiments show that the debugged knowledge bases by the method significantly improve the system performance on the validation sets as well as on the training sets. The experimental results also empirically verify the manifestation of the sociopathic interactions among the rules and the improbability of locally debugging this type of inconsistent knowledge bases.