How to Infer from Inconsisent Beliefs without Revising?
Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. Non-monotonic Reasoning. Pages 1449-1456.
Abstract
This paper investigates several methods for coping with inconsistency caused by multiple source information, by introducing suitable consequence relations capable of inferring non-trivial conclusions from an inconsistent stratified knowledge base. Some of these methods presuppose a revision step, namely a selection of one or several consistent subsets of formulas, and then classical inference is used for inferring from these subsets. Two alternative methods that do not require any revision step are studied: inference based on arguments, and a new approach called safely supported inference, where inconsistency is kept local. These two last methods look suitable when the inconsistency is due to the presence of several sources of information. The paper offers a comparative study of the various inference modes under inconsistency.
@inproceedings{ijcai1995p187,
title = {How to Infer from Inconsisent Beliefs without Revising?},
author = {Benferhat, Salem and Dubois, Didier and Prade, Henri},
booktitle = {Proceedings of the Fourteenth International Joint Conference on
Artificial Intelligence, {IJCAI-95}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Chris S. Mellish},
pages = {1449--1456},
year = {1995},
note = {Non-monotonic Reasoning},
url = {https://ijcai.org/proceedings/1995/187},
}
TY - CPAPER TI - How to Infer from Inconsisent Beliefs without Revising? AU - Benferhat, Salem AU - Dubois, Didier AU - Prade, Henri T2 - Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI-95 PB - International Joint Conferences on Artificial Intelligence PY - 1995 SP - 1449 EP - 1456 UR - https://ijcai.org/proceedings/1995/187 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
