New Inference Algorithms Based on Rules Partition

This paper presents new inference algorithms based on rules partition. Optimisation relies on reducing the number of rules searched in each run of inference and reducing the number of unnecessary recursive calls and avoiding the extraction of too many new facts. The first part of the paper presents the definition of the model of knowledge base based on rules partition. The second part describes the modifications of both forward and backward inference algorithms using knowledge base with rules assigned to the groups given by the partition strategy. At the end, the paper consists of an example of knowledge base with the description of inference processes for this particular knowledge base.