Credit Apportionment Scheme for Rule-Based Systems: Implementation and Comparative Study

Abstract

Credit Apportionment scheme is the backbone of the performance of adaptive rule based system. The more cases the credit apportionment scheme can consider, the better is the overall systems performance. Currently rule based systems are used in various areas such as expert systems and machine learning which means that new rules to be generated and others to be eliminated. Several credit apportionment schemes have been proposed and some of them are even used but still most of these schemes suffer from disability of distinguishing between good rules and bad rules. Correct rules might be weakened because they are involved in an incorrect inference path (produces incorrect conclusion) and incorrect rules might be strengthen because they are involved in an inference path which produces correct conclusion. In this area a lot of research has been done, we consider three algorithms, Bucket Brigade algorithm (BB), Modified Bucket Algorithm (MBB) and General Credit Apportionment (GCA). The algorithms BB and MBB are from the same family in which they use the same credit allocation techniques where GCA uses different approach.

In this research, we make a comparison study by implementing the three algorithms and apply them on a simulated “Soccer” expert rule-based system. To evaluate the algorithms, two experiments have been conducted.