Probabilistic Reasoning with Inconsistent Beliefs Using Inconsistency Measures
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Special Track on Knowledge Representation and Reasoning. Pages 3156-3162.
Abstract
The classical probabilistic entailment problem is to determine upper and lower bounds on the probability of formulas, given a consistent set of probabilistic assertions. We generalize this problem by omitting the consistency assumption and, thus, provide a general framework for probabilistic reasoning under inconsistency. To do so, we utilize inconsistency measures to determine probability functions that are closest to satisfying the knowledge base. We illustrate our approach on several examples and show that it has both nice formal and computational properties.
@inproceedings{ijcai2015p445,
title = {Probabilistic Reasoning with Inconsistent Beliefs Using Inconsistency Measures},
author = {Potyka, Nico and Thimm, Matthias},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {3156--3162},
year = {2015},
month = {7},
note = {Special Track on Knowledge Representation and Reasoning},
url = {https://ijcai.org/proceedings/2015/445},
}
TY - CPAPER TI - Probabilistic Reasoning with Inconsistent Beliefs Using Inconsistency Measures AU - Potyka, Nico AU - Thimm, Matthias T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 3156 EP - 3162 UR - https://ijcai.org/proceedings/2015/445 ER -Copied
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