A Rete-based integration of forward and backward chaining inferences

Abstract

Studies a new method for integrating forward and backward chaining controls based on a Rete network. The authors introduce the concept of the hypothesis objects, and augment the production rule format by adding the hypothesis condition elements in the LHS part of a rule, which control the forward and backward chaining invocation of rules. To be applied to the backward chaining, a modified version of the Rete network has been formulated by adding the hypothesis-and-nodes and backward direction edges to the node network generated by the Rete algorithm. The authors then develop a backward chaining algorithm which takes advantage of already existing partial match results obtained by the previous forward chaining. The forward and backward chaining can be naturally integrated via the generation of hypotheses from forward chaining inference. The whole mixed inference process has the potential advantages over purely forward and backward chaining strategy.