AAAI Publications, Twelfth International Conference on the Principles of Knowledge Representation and Reasoning

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Novel Semantical Approaches to Relational Probabilistic Conditionals
Gabriele Kern-Isberner, Matthias Thimm

Last modified: 2010-04-27

Abstract


It seems to be a common view that in order to interpret probabilistic first-order sentences, either a statistical approach that counts (tuples of) individuals has to be used, or the knowledge base has to be grounded to make a possible worlds semantics applicable, for a subjective interpretation of probabilities. In this paper, we propose novel semantical perspectives on first-order (or relational) probabilistic conditionals that are motivated by considering them as subjective, but population-based statements. We propose two different semantics for relational probabilistic conditionals, and a set of postulates for suitable inference operators in this framework. Finally, we present two inference operators by applying the maximum entropy principle to the respective model theories. Both operators are shown to yield reasonable inferences according to the postulates.

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