Belief merging with the aim of truthlikeness
The merging/fusion of belief/data collections in propositional logic form is a topic that has received due attention within the domains of database and AI research. A distinction can be made between two types of scenarios to which the process of merging can be applied. In the first type, the collections represent preferences, such as the voting choices of a group of people, that need to be aggregated so as to give a consistent result that in some way best represents the collective judgement of the group. In the second type, the collections represent factual data that is to be aggregated with an aim of obtaining a result that maximises factual correctness. After introducing a general framework for belief merging via some prominent literature on the topic, this paper then introduces and considers a method for belief merging with the second type of scenario in mind. Its suitability is corroborated by demonstrating how it can be seen as a special case of a merging procedure that combines aggregation of probabilities and maximisation of expected truthlikeness.
KeywordsBelief merging Information fusion Truthlikeness Judgment aggregation
Thanks to Gustavo Cevolani for general discussion on the topic of truthlikeness and belief merging, Che-Ping Su for feedback on an early draft of this paper and Aditya Ghose for hosting a presentation of this material and providing feedback. Also, thanks to the anonymous referees who provided feedback that helped to shape this paper.
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