Artificial Intelligence
Journal of Complexity
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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
A symbolic approach to reasoning with linguistic quantifiers
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Towards precision of probabilistic bounds propagation
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Anytime deduction for probabilistic logic
Artificial Intelligence
Decision Support Systems - Special issue on logic modeling
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Computational problems related to the design of normal form relational schemas
ACM Transactions on Database Systems (TODS)
An Equivalence Between Relational Database Dependencies and a Fragment of Propositional Logic
Journal of the ACM (JACM)
Uncertain Reasoning in Concept Lattices
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Probabilistic Reasoning Under Coherence in System P
Annals of Mathematics and Artificial Intelligence
Probabilistic deduction with conditional constraints over basic events
Journal of Artificial Intelligence Research
Hybrid probabilistic programs: algorithms and complexity
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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We present locally complete inference rules for probabilistic deduction from taxonomic and probabilistic knowledge-bases over conjunctive events. Crucially, in contrast to similar inference rules in the literature, our inference rules are locally complete for conjunctive events and under additional taxonomic knowledge. We discover that our inference rules are extremely complex and that it is at first glance not clear at all where the deduced tightest bounds come from. Moreover, analyzing the global completeness of our inference rules, we find examples of globally very incomplete probabilistic deductions. More generally, we even show that all systems of inference rules for taxonomic and probabilistic knowledge-bases over conjunctive events are globally incomplete. We conclude that probabilistic deduction by the iterative application of inference rules on interval restrictions for conditional probabilities, even though considered very promising in the literature so far, seems very limited in its field of application.