Irrelevance and conditioning in firstorder probabilistic logic (1996)by D. Koller and J. Y. Halpern
Abstract:
Firstorder 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 firstorder 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.
Download Information
D. Koller and J. Y. Halpern (1996). "Irrelevance and conditioning in firstorder probabilistic logic." Proceedings of the 13th National Conference on Artificial Intelligence (AAAI) (pp. 569576).


Bibtex citation
@inproceedings{Koller+Halpern:AAAI96,
author = "D. Koller and J. Y. Halpern",
booktitle = "Proceedings of the 13th National Conference on Artificial Intelligence (AAAI)",
title = "Irrelevance and conditioning in firstorder
probabilistic logic",
pages = "569576",
year = "1996",
}
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