The mathematics of inheritance systems
The mathematics of inheritance systems
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
An analysis of first-order logics of probability
Artificial Intelligence
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Reasoning about noisy sensors in the situation calculus
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
First-order conditional logic for default reasoning revisited
ACM Transactions on Computational Logic (TOCL)
Satisfiability and Meaning of Formulas and Sets of Formulas in Approximation Spaces
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P 2004)
Stochastic processes and temporal data mining
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Credal networks under maximum entropy
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Satisfiability and Meaning of Formulas and Sets of Formulas in Approximation Spaces
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P 2004)
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First-order probabilistic logic is a powerful knowledge representation language. Unfortunately, deductive reasoning based on the standard semantics for this logic does not support certain desirable patterns of reasoning, such as indifference to irrelevant information or substitution of constants into universal rules. We show that both these patterns rely on a first-order version of probabilistic independence, and provide semantic conditions to capture them. The resulting insight enables us to understand the effect of conditioning on independence, and allows us to describe a procedure for determining when independencies are preserved under conditioning. We apply this procedure in the context of a sound and powerful inference algorithm for reasoning from statistical knowledge bases.