Fast hypothetical reasoning using analogy on inference-path networks

One crucial problem with a 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, by using analogy of results which were previously proved to be true. An inference-path network can be effectively used for selecting useful hypotheses from an analogous case, and for generating new additional 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 use ordinary search methods. This paper shows, however, that this limit can be overcome in average inference time by using analogy.