Artificial Intelligence
Journal of Complexity
A logic for reasoning about probabilities
Information and Computation - Selections from 1988 IEEE symposium on logic in computer science
Constraint propagation with imprecise conditional probabilities
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Anytime deduction for probabilistic logic
Artificial Intelligence
Artificial intelligence in perspective
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)
Computers and Operations Research
Discrete Mathematics
Probabilistic Satisfiability and Decomposition
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
An Investigation of the Laws of Thought
An Investigation of the Laws of Thought
Document similarity: a new measure using OWA
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 7
Sequential decision making with partially ordered preferences
Artificial Intelligence
A hybrid method for probabilistic satisfiability
CADE'11 Proceedings of the 23rd international conference on Automated deduction
Gases Brownian Motion Optimization: an Algorithm for Optimization (GBMO)
Applied Soft Computing
Probabilistic satisfiability and coherence checking through integer programming
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Solutions for hard and soft constraints using optimized probabilistic satisfiability
SAT'13 Proceedings of the 16th international conference on Theory and Applications of Satisfiability Testing
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The probabilistic satisfiability problem is to verify the consistency of a set of probability values or intervals for logical propositions. The (tight) probabilistic entailment problem is to find best bounds on the probability of an additional proposition. The local approach to these problems applies rules on small sets of logical sentences and probabilities to tighten given probability intervals. The global approach uses linear programming to find best bounds. We show that merging these approaches is profitable to both: local solutions can be used to find global solutions more quickly through stabilized column generation, and global solutions can be used to confirm or refute the optimality of the local solutions found. As a result, best bounds are found, together with their step-by-step justification.