Parallel Belief Revision via Order Aggregation
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 4419-4426.
https://doi.org/10.24963/ijcai.2025/492
Keywords
- Knowledge Representation and Reasoning: KRR: Belief change
- Game Theory and Economic Paradigms: GTEP: Computational social choice
Abstract
Despite efforts to better understand the constraints that operate on single-step parallel (aka ``package'', ``multiple'') revision, very little work has been carried out on how to extend the model to the iterated case. A recent paper by Delgrande & Jin outlines a range of relevant rationality postulates. While many of these are plausible, they lack an underlying unifying explanation. We draw on recent work on iterated parallel contraction to offer a general method for extending serial iterated belief revision operators to handle parallel change. This method, based on a family of order aggregators known as TeamQueue aggregators, provides a principled way to recover the independently plausible properties that can be found in the literature, without yielding the more dubious ones.
@inproceedings{ijcai2025p492,
title = {Parallel Belief Revision via Order Aggregation},
author = {Chandler, Jake and Booth, Richard},
booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-25}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {James Kwok},
pages = {4419--4426},
year = {2025},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2025/492},
url = {https://doi.org/10.24963/ijcai.2025/492},
}
TY - CPAPER TI - Parallel Belief Revision via Order Aggregation AU - Chandler, Jake AU - Booth, Richard T2 - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2025 SP - 4419 EP - 4426 DO - 10.24963/ijcai.2025/492 UR - https://doi.org/10.24963/ijcai.2025/492 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: