A Plausibility Description Logic for Handling Information Sources with Heterogeneous Data Representation Formats
This paper proposes and investigates an approach to deduction in probabilistic logic, using as its medium a language that generalizes the propositional version of Nilsson's probabilistic logic by incorporating conditional probabilities. Unlike many other approaches to deduction in probabilistic logic, this approach is based on inference rules and therefore can produce proofs to explain how conclusions are drawn. We show how these rules can be incorporated into an anytime deduction procedure that proceeds by computing increasingly narrow probability intervals that contain the tightest entailed probability interval. Since the procedure can be stopped at any time to yield partial information concerning the probability range of any entailed sentence, one can make a tradeoo between precision and computation time. The deduction method presented here contrasts with other methods whose ability to perform logical reasoning is either limited or requires nding all truth assignments consistent with the given sentences.