A Syntax-based Framework for Merging Imprecise Probabilistic Logic Programs
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 1990-1994.
Abstract
In this paper, we address the problem of merging multiple imprecise probabilistic beliefs represented as Probabilistic Logic Programs (PLPs) obtained from multiple sources. Beliefs in each PLP are modeled as conditional events attached with probability bounds. The major task of syntax-based merging is to obtain the most rational probability bound for each conditional event from the original PLPs to form a new PLP. We require the minimal change principle to be followed so that each source gives up its beliefs as little as possible. Some instantiated merging operators are derived from our merging framework. Furthermore, we propose a set of postulates for merging PLPs, some of which extend the postulates for merging classical knowledge bases, whilst others are specific to the merging of probabilistic beliefs.Anbu Yue, Weiru Liu
@inproceedings{ijcai2009p328,
title = {A Syntax-based Framework for Merging Imprecise Probabilistic Logic Programs},
author = {Yue, Anbu and Liu, Weiru},
booktitle = {Proceedings of the Twenty-First International Joint Conference on
Artificial Intelligence, {IJCAI-09}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Hiroaki Kitano},
pages = {1990--1994},
year = {2009},
url = {https://ijcai.org/proceedings/2009/328},
}
TY - CPAPER TI - A Syntax-based Framework for Merging Imprecise Probabilistic Logic Programs AU - Yue, Anbu AU - Liu, Weiru T2 - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09 PB - International Joint Conferences on Artificial Intelligence PY - 2009 SP - 1990 EP - 1994 UR - https://ijcai.org/proceedings/2009/328 ER -Copied
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