A rule activation method for extended belief rule base with VP-tree and MVP-tree

The belief rules are stored out of order in the extended belief rule base (EBRB), which will weaken its reasoning performance in that all rules are visited when calculating each rule's activation weight. This paper focuses on reducing the number of rules which are visited in the calculation of each rule's activation weight. A new rule activation method based on VP-tree and MVP-tree is proposed to build index structure to store rules. The proposed rule activation method is based on rule similarity query, where only partial rules will be retrieved and visited while calculating each rule's activation weight. Note that, the performance of EBRB systems based on tree index is affected greatly by the value of query threshold. However, sometimes it is difficult to determine the value of query threshold, so this paper also proposes an approach based on the k-means clustering algorithm to choose the appropriate query threshold. Some case studies show how the use of the proposed optimization method enhances the reasoning performance of EBRB systems. The proposed method has been validated to be advantageous to visit partial suitable rules instead of all rules. Beside the work performed in the EBRB, the proposed method alone can also be used in different application areas.