Probabilistic Extention to Realistic Abductive Reasoning Model
نویسنده
چکیده
In this paper, we give method for probabilistic assignment to the Realistic Abduc-tive Reasoning Model, proposed in 1]. 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 the probability to the explanations. First, the inference mechanism is carried out for a given set of symptoms by using Realistic Abductive Reasoning model and then probability is assigned to each of the explanations so as to pick up the plausible explanations in the decreasing order of probability.
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