Classifying and detecting anomalies in hybrid knowledge-based systems

The increasing need for hybrid Knowledge-Based Systems (KBS) that accommodate more complex applications has led to the need for new verification concerns that are more specific to the hybrid representation using objects and rule-based inference. Traditionally, verification of expert systems has focused solely on rule-based inference systems. Hybrid KBSs present additional verification problems not found in traditional rule-based systems. This paper is an investigation into the anomalies that may be present in a hybrid representation that warrant detection for the verification of the KBS. Many anomalies are due to the interaction of the component parts of the hybrid KBS. For example, subsumption anomalies arise due to an interaction between inheritance of objects and rule-based inference. In this paper, we extend the context of subsumption anomalies and introduce additional types of anomalies that may be present in the KBS. The goal of this research is to make hybrid KBSs more reliable by detecting such anomalies.