Multi-Agent Belief Base Revision
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 1959-1965.
https://doi.org/10.24963/ijcai.2021/270
Keywords
- Knowledge Representation and Reasoning: Reasoning about Knowledge and Belief
- Knowledge Representation and Reasoning: Logics for Knowledge Representation
- Knowledge Representation and Reasoning: Belief Change
Abstract
We present a generalization of belief base revision
to the multi-agent case. In our approach agents
have belief bases containing both propositional beliefs
and higher-order beliefs about their own beliefs
and other agents’ beliefs. Moreover, their belief
bases are split in two parts: the mutable part,
whose elements may change under belief revision,
and the core part, whose elements do not change.
We study a belief revision operator inspired by
the notion of screened revision. We provide complexity
results of model checking for our approach
as well as an optimal model checking algorithm.
Moreover, we study complexity of epistemic planning
formulated in the context of our framework.
@inproceedings{ijcai2021p270,
title = {Multi-Agent Belief Base Revision},
author = {Lorini, Emiliano and Schwarzentruber, François},
booktitle = {Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, {IJCAI-21}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Prof. Zhi-Hua Zhou},
pages = {1959--1965},
year = {2021},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2021/270},
url = {https://doi.org/10.24963/ijcai.2021/270},
}
TY - CPAPER TI - Multi-Agent Belief Base Revision AU - Lorini, Emiliano AU - Schwarzentruber, François T2 - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2021 SP - 1959 EP - 1965 DO - 10.24963/ijcai.2021/270 UR - https://doi.org/10.24963/ijcai.2021/270 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: