MEASURING THE COMPLEXITY OF RULE-BASED EXPERT-SYSTEMS

Many quantitative metrics have already been proposed to measure software complexity for different applications. A similar but different application of these measurements could also be developed for formal evaluation of expert systems (ES) which have some characteristics in common with conventional software. But they also have many distinct features of their own, for example, the rules employed in an expert system do not directly determine the execution order, instead they represent the expertise for solving problems in a general way, so the meanings implied by rules are different from those implied by conventional statements. In this paper, some problems concerning the complexity measurement of rule-based ES are discussed, they include complexity description, complexity model, criteria for measurement, and the measurement. A new approach to measure rulebase complexity is presented. It takes three factors into account: 1) content of the rulebase; 2) connectivity among the rules; and 3) size of the rulebase. In order to assess the effectiveness of this new approach, the complexity of 21 sample rulebases has been measured. The results show that this new measurement is more accurate than that of the ''number of rules'' or ''Buchanan's solution space complexity''; suggesting that it is a better indicator of the rulebase complexity. Furthermore, the computation model of this measurement exhibits characteristics of being meaningful, reasonable, reliable and cost-effective. Another advantage is that the measurement results could be used as an estimator for maintenance or developed efforts, especially in applications where experimental data are lacking.