Complexity-Based Evaluation Of Rule-Based Expert Systems

We describe techniques for evaluating rule-based expert systems by complexity-analyses. The degree of redundancy and superfluity in the knowledge base is measured by comparing its performance to pruned versions of itself. The pruning method, called weakest-link pruning, takes advantage of the structure of the rule-base to perform a cost-complexity pruning of the set of rules. Two large rule-based expert systems for real-world applications are evaluated using this method. These pruning techniques enable an evaluation of the degree of redundancy of the knowledge base, the identification of the critical portions of the knowledge base and an evaluation of the adequacy of the benchmark cases.