Probabilistic abduction without priors
International Journal of Approximate Reasoning
Resolving Inconsistencies in Probabilistic Knowledge Bases
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Indefinite Probabilities for General Intelligence
Proceedings of the 2007 conference on Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006
Belief Revision through Forgetting Conditionals in Conditional Probabilistic Logic Programs
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Revising imprecise probabilistic beliefs in the framework of probabilistic logic programming
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
A syntax-based framework for merging imprecise probabilistic logic programs
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Imprecise probabilistic query answering using measures of ignorance and degree of satisfaction
Annals of Mathematics and Artificial Intelligence
Probabilistic Belief Contraction
Minds and Machines
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This article presents new methods for probabilistic belief revision and information fusion. By making use of the information theoretical principles of optimum entropy (ME principles), we define a generalized revision operator that aims at simulating the human learning of lessons, and we introduce a fusion operator that handles probabilistic information faithfully. This ME-fusion operator satisfies basic demands, such as commutativity and the Pareto principle. A detailed analysis shows it to merge the corresponding epistemic states. Furthermore, it induces a numerical fusion operator that computes the information theoretical mean of probabilities. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 837–857, 2004.