Belief Merging Operators as Maximum Likelihood Estimators
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 1763-1769. https://doi.org/10.24963/ijcai.2020/244
We study how belief merging operators can be considered as maximum likelihood estimators, i.e., we assume that there exists a (unknown) true state of the world and that each agent participating in the merging process receives a noisy signal of it, characterized by a noise model. The objective is then to aggregate the agents' belief bases to make the best possible guess about the true state of the world. In this paper, some logical connections between the rationality postulates for belief merging (IC postulates) and simple conditions over the noise model under consideration are exhibited. These results provide a new justification for IC merging postulates. We also provide results for two specific natural noise models: the world swap noise and the atom swap noise, by identifying distance-based merging operators that are maximum likelihood estimators for these two noise models.
Knowledge Representation and Reasoning: Belief Change, Belief Merging