Revising imprecise probabilistic beliefs in the framework of probabilistic logic programming

Authors:
Anbu Yue;Weiru Liu
Affiliations:
School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK;School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK
Venue:
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Year:
2008

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Abstract

Probabilistic logic programming is a powerful technique to represent and reason with imprecise probabilistic knowledge. A probabilistic logic program (PLP) is a knowledge base which contains a set of conditional events with probability intervals. In this paper, we investigate the issue of revising such a PLP in light of receiving new information. We propose postulates for revising PLPs when a new piece of evidence is also a probabilistic conditional event. Our postulates lead to Jeffrey's rule and Bayesian conditioning when the original PLP defines a single probability distribution. Furthermore, we prove that our postulates are extensions to Darwiche and Pearl (DP) postulates when new evidence is a propositional formula. We also give the representation theorem for the postulates and provide an instantiation of revision operators satisfying the proposed postulates.