A Minimal Connection Model of Abductive Diagnostic Reasoning

In this paper, we present a minimal connection model of abductive diagnostic reasoning. The domain knowledge is represented by a causal network. An explanation of a set of observations is a chain of causation events. These causation events constitute a scenario where all the observations can be observed. We define the best explanation to be the most probable explanation. The underlying causal model enables us to compute the probabilities of explanations from the conditional probabilities of the participating causation events. We then present an algorithm for finding the most probable explanations. Although probabilistic inference using belief networks is NP-hard in general, our algorithm is polynomial to the number of nodes in the networks and is exponential only to the number of observations to be explained, which, in any single case, is usually small.