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Maximum Entropy in Nilsson's Probabilistic Logic

Kane, Thomas B

Authors

Thomas B Kane



Abstract

Nilsson's Probabilistic Logic is a set theoretic mechanism for reasoning with uncertainty. We propose a new way of looking at the probability constraints enforced by the framework, which allows the expert to include conditional probabilities in the semantic tree, thus making Probabilistic Logic more expressive. An algorithm is presented which will find the maximum entropy point probability for a rule of entailment without resorting to solution by iterative approximation. The algorithm works for both the propositional and the predicate logic. Also presented are a number of methods for employing the conditional probabilities.

Citation

Kane, T. B. (2017). Maximum Entropy in Nilsson's Probabilistic Logic. In Proceedings of the 11th International Joint Conference on Artificial Intelligence (452-457)

Conference Name International Joint Conference on Artificial Intelligence
Start Date Aug 15, 1989
End Date Aug 19, 1989
Acceptance Date Jan 10, 1989
Publication Date Aug 15, 2017
Deposit Date Feb 13, 2018
Publisher International Joint Conferences on Artificial Intelligence Organization
Pages 452-457
Book Title Proceedings of the 11th International Joint Conference on Artificial Intelligence.
Chapter Number NA
ISBN 1-555860-094-9
Keywords Reasoning, probability,
Public URL http://researchrepository.napier.ac.uk/Output/1007020