Towards a Possibilistic Logic Handling of Preferences
Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence. Uncertainty and Probabilistic Reasoning. Pages 1370-1374.
Abstract
The classical way of encoding preferences in decision theory is by means of utility or value functions. However agents are not always able to deliver such a function directly. In this paper, we relate three different ways of specifying preferences, namely by means of a set of particular types of constraints on the utility function, by means of an ordered set of prioritized goals expressed by logical propositions, and by means of an ordered set of subsets of possible candidates reaching the same level of satisfaction. These different expression modes can be handled in a weighted logical setting, here the one of possibilistic logic. The aggregation of preferences pertaining to different criteria can then be handled by fusing sets of prioritized goals. Apart from a better expressivity , the benefits of a logical representation of preferences are to put them in a suitable format for reasoning purposes, or for modifying or revising them.
@inproceedings{ijcai1999p194,
title = {Towards a Possibilistic Logic Handling of Preferences},
author = {Benferhat, Salem and Dubois, Didier and Prade, Henri},
booktitle = {Proceedings of the Sixteenth International Joint Conference on
Artificial Intelligence, {IJCAI-99}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Thomas Dean},
pages = {1370--1374},
year = {1999},
note = {Uncertainty and Probabilistic Reasoning},
url = {https://ijcai.org/proceedings/1999/194},
}
TY - CPAPER TI - Towards a Possibilistic Logic Handling of Preferences AU - Benferhat, Salem AU - Dubois, Didier AU - Prade, Henri T2 - Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI-99 PB - International Joint Conferences on Artificial Intelligence PY - 1999 SP - 1370 EP - 1374 UR - https://ijcai.org/proceedings/1999/194 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: