An approach to enhancing the maintainability of expert systems

The task of maintaining expert systems has become increasingly difficult as the size of their knowledge bases increases. To address this issue, a unified AI (artificial intelligence) programming environment (CLASP) has been developed; this environment tightly integrates three AI programming schemes: the term subsumption languages in knowledge representation, the production system architecture, and methods in object-oriented programming. The CLASP architecture separates the knowledge about when to trigger a task from the knowledge about how to accomplish a given task. It also extends the pattern matching capabilities of conventional rule-based systems by using the semantic information related to rule conditions. In addition, it uses a pattern classifier to compute a principled measure about the specificity of rules. Using a monkey-bananas problem, the authors demonstrate that an expert system built in CLASP is easier to maintain because the architecture facilitates the development of a consistent and homogeneous knowledge base, enhances the predictability of rules, and improves the organization and reusability of knowledge.