Preferred Subtheories: An Extended Logical Framework for Default Reasoning
Proceedings of the Eleventh International Joint Conference on Artificial Intelligence. Commonsense Reasoning. Pages 1043-1047.
Abstract
We present a general framework for defining nonmonotonic systems based on the notion of preferred maximal consistent subsets of the premises. This framework subsumes David Poole's THEORIST approach to default reasoning as a particular instance. A disadvantage of THEORIST is that it does not allow to represent priorities between defaults adequately (as distinct from blocking defaults in specific situations). We therefore propose two generalizations of Poole's system: in the first generalization several layers of possible hypotheses representing different degrees of reliability are introduced. In a second further generalization a partial ordering between premises is used to distinguish between more and less reliable formulas. In both approaches a formula is provable from a theory if it is possible to construct a consistent argument for it based on the most reliable hypotheses. This allows for a simple representation of priorities between defaults.
@inproceedings{ijcai1989p167,
title = {Preferred Subtheories: An Extended Logical Framework for Default Reasoning},
author = {Brewka, Gerhard},
booktitle = {Proceedings of the Eleventh International Joint Conference on
Artificial Intelligence, {IJCAI-89}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {N. S. Sridharan},
pages = {1043--1047},
year = {1989},
month = {8},
note = {Commonsense Reasoning},
url = {https://ijcai.org/proceedings/1989/167},
}
TY - CPAPER TI - Preferred Subtheories: An Extended Logical Framework for Default Reasoning AU - Brewka, Gerhard T2 - Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, IJCAI-89 PB - International Joint Conferences on Artificial Intelligence PY - 1989 SP - 1043 EP - 1047 UR - https://ijcai.org/proceedings/1989/167 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: