Abstract
In this paper we make defeasible inferences from conditional probabilities using the Principle of Total Evidence. This gives a logic that is a simple extension of the axiomatization of probabilistic logic as defined by Halpern's AX 1. For our consequence relation, the reasoning is further justified by an assumption of the typicality of individuals mentioned in the data. For databases which do not determine a unique probability distribution, we select by default the distribution with Maximum Entropy. We situate this logic in the context of preferred models semantics.
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© 1993 Springer-Verlag
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Cussens, J., Hunter, A. (1993). Using maximum entropy in a defeasible logic with probabilistic semantics. In: Bouchon-Meunier, B., Valverde, L., Yager, R.R. (eds) IPMU '92—Advanced Methods in Artificial Intelligence. IPMU 1992. Lecture Notes in Computer Science, vol 682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56735-6_42
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DOI: https://doi.org/10.1007/3-540-56735-6_42
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