Syntactic Computation of Hybrid Possibilistic Conditioning under Uncertain Inputs
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 739-744.
Abstract
We extend hybrid possibilistic conditioning to deal with inputs consisting of a set of triplets composed of propositional formulas, the level at which the formulas should be accepted, and the way in which their models should be revised. We characterize such conditioning using elementary operations on possibility distributions. We then solve a difficult issue that concerns the syntactic computation of the revision of possibilistic knowledge bases, made of weighted formulas, using hybrid conditioning. An important result is that there is no extra computational cost in using hybrid possibilistic conditioning and in particular the size of the revised possibilistic base is polynomial with respect to the size of the initial base and the input.
@inproceedings{ijcai2013p116,
title = {Syntactic Computation of Hybrid Possibilistic Conditioning under Uncertain Inputs},
author = {Benferhat, Salem and Pereira, Célia da Costa and Tettamanzi, Andrea},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {739--744},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/116},
}
TY - CPAPER TI - Syntactic Computation of Hybrid Possibilistic Conditioning under Uncertain Inputs AU - Benferhat, Salem AU - Pereira, Célia da Costa AU - Tettamanzi, Andrea T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 739 EP - 744 UR - https://ijcai.org/proceedings/2013/116 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
