Sampling-Based Belief Revision
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 1272-1277.
Abstract
Model sampling has proved to be a practically viable method for decision-making under uncertainty, for example in imperfect-information games with large state spaces. In this paper, we examine the logical foundations of sampling-based belief revision. We show that it satisfies six of the standard AGM postulates but not Vacuity nor Subexpansion. We provide a corresponding representation theorem that generalises the standard result from a single to a family of faithful assignments for a given belief set. We also provide a formal axiomatisation of sampling-based belief revision in the Situation Calculus as an alternative way of reasoning about actions, sensing, and beliefs.
@inproceedings{ijcai2016p184,
title = {Sampling-Based Belief Revision},
author = {Thielscher, Michael},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {1272--1277},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/184},
}
TY - CPAPER TI - Sampling-Based Belief Revision AU - Thielscher, Michael T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 1272 EP - 1277 UR - https://ijcai.org/proceedings/2016/184 ER -Copied
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