A Logical Approach to Deal with Incomplete Causal Models in Diagnostic Problem Solving

Different approaches have been proposed in the last few years to the formalization of deep diagnostic problem solving. In this paper we briefly analyze the two main approaches proposed so far for modeling diagnostic problem solving, i.e. the logical one (a la Reiter or a la de Kleer) and the probabilistic one (a la Pearl). Starting from such an analysis, we propose a different approach to the formalization of deep reasoning, based on the adoption of peculiar forms of non-monotonic logics. The approach allows us to perform sophisticated and qualitative forms of "approximate" (common-sense) reasoning. More specifically, we have designed a formalism for deep modeling which permits the description (representation) of incomplete and/or uncertain causal knowledge. A formal logical semantics has been defined for the modeling scheme, so that all the deep reasoning process can be logically characterized and defined. In particular a complex form of hypothetical (assumption-based) reasoning scheme has been defined to deal with incomplete models.

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