Probabilistic Extension to Realistic Abductive Reasoning Model

摘要

In this paper, we give a method for probabilistic assignment to the Realistic Abductive Reasoning Model. The knowledge is assumed to be represented in the form of causal chaining, namely, hyper-bipartite network. Hyper-bipartite network is the most generalized form of knowledge representation for which, so far, there has been no way of assigning probability to the explanations. First, the inference mechanism using realistic abductive reasoning model is briefly described and then probability is assigned to each of the explanations so as to pick up the explanations in the decreasing order of plausibility.