Fixpoint semantics for rule-base anomalies

A crucial component of an intelligent system is its knowledge base that contains knowledge about a problem domain. Knowledge base development involves domain analysis, context space definition, ontological specification, and knowledge acquisition, codification and verification. Knowledge base anomalies can affect the correctness and performance of an intelligent system. In this paper, we adopt a fixpoint semantics that is based on a multi-valued logic for a knowledge base. We then use the fixpoint semantics to provide formal definitions for four types of knowledge base anomalies: inconsistency, redundancy, incompleteness, circularity. We believe such formal definitions of knowledge base anomalies helps pave the way for a more effective knowledge base verification process.