Search

The problems encountered in applying object-oriented programming to expert systems are described. A production system called Clasp, which addresses these difficulties, is presented. Clasp integrates methods, production rules, and terminological definitions for classes. The approach is a further generalization of Common Loops and the Common Lisp Operating System, which have all extended notion of methods in which all argument types can describe the applicability of methods. The system was designed to improve the modularity and reusability of the rule base, to support the development of a more consistent and homogeneous knowledge base, and to enhance the predictability of rules. >

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 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. >