Fast Hypothetical Reasoning System using Analogy on Inference-path Network

Abstract

The crucial problem of hypothetical reasoning system is its slow inference speed, while it is a very useful framework in knowledge processing. We present a fast mechanism for the hypothetical reasoning, which uses the analogy of previous inference cases already proved to be true. An inference-path network can be effectively used for selecting useful hypotheses from analogical cases, and for generating new hypotheses which are necessary for proving a new goal. The inference speed of the hypothetical reasoning, whose computational complexity has been proved to be NP-complete or NP-hard, can not be improved from the exponential-order limit as long as we stay in ordinary search methods. This paper shows, however, that this limit can be improved to a large extent in average inference time by using analogy.