An Efficient Hybrid Rule Based Inference Engine with Explanation Capability

An inference engine for a hybrid representation scheme based on neurules is presented. Neurules are a kind of hybrid rules that combine a symbolic (production rules) and a connectionist representation (adaline unit). The inference engine uses a connectionist technique, which is based on the ‘firing potential’, a measurement of the firing tendency of a neurule, and symbolic pattern matching. It is proved to be more efficient and natural than pure connectionist inference engines. Explanation of ‘how’ type can be provided in the form of if-then symbolic rules.