Verification and correctness issues for nonmonotonic knowledge bases

Anomalies such as redundant, contradictory or deecient knowledge in a knowledge base indicate possible errors. Various methods for detecting such anomalies have been introduced, analyzed and applied in the past years, but they usually deal with rule-based systems. So far, little attention has been payed to the veriication and validation of more complex representations, such as nonmonotonic knowledge bases, although there are good reasons to expect that these technologies will be increasingly used in practical applications. This paper does a step towards the veriication of knowledge bases which include defaults by providing a theoretical foundation of correct-ness concepts and a classiication of possible anomalies. It also points out how existing veriication methods may be applied to detect some anomalies in nonmonotonic knowledge bases, and dicsusses methods of avoiding potential inconsistencies (in the context of default reasoning inconsistency means nonexistence of extensions).