Artificial Intelligence
Journal of Complexity
Constraint propagation with imprecise conditional probabilities
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Anytime deduction for probabilistic logic
Artificial Intelligence
Boole's conditions of possible experience and reasoning under uncertainty
Discrete Applied Mathematics - Special volume: Aridam VI and VII, Rutcor, New Brunswick, NJ, USA (1991 and 1992)
Axiomatization of frequent itemsets
Theoretical Computer Science
Branch-And-Price: Column Generation for Solving Huge Integer Programs
Operations Research
Probabilistic polynomial-time semantics for a protocol security logic
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
Anytime Reasoning Mechanism for Conversational Agents
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
Anytime Reasoning Mechanism for Conversational Agents
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
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We study two basic problems of probabilistic reasoning: the probabilistic logic and the probabilistic entailment problems. The first one can be defined as follows. Given a set of logical sentences and probabilities that these sentences are true, the aim is to determine whether these probabilities are consistent or not. Given a consistent set of logical sentences and probabilities, the probabilistic entailment problem consists in determining the range of the possible values of the probability associated with additional sentences while maintaining a consistent set of sentences and probabilities. This paper proposes a general approach based on an anytime deduction method that allows the follow-up of the reasoning when checking consistency for the probabilistic logic problem or when determining the probability intervals for the probabilistic entailment problem. Considering a series of subsets of sentences and probabilities, the approach proceeds by computing increasingly narrow probability intervals that either show a contradiction or that contain the tightest entailed probability interval. Computational experience have been conducted to compare the proposed anytime deduction method, called ad-psat with an exact one, psatcol, using column generation techniques, both with respect to the range of the probability intervals and the computing times.