Surprise Minimization Revision Operators
This work addresses belief revision in AI and logic, providing a theoretical extension to existing frameworks, but it appears incremental as it builds on standard models without clear practical applications.
The paper tackles the problem of belief revision by extending existing models with a new measure called relative surprise, which incorporates both prior beliefs and contextual information from new evidence. They characterize this surprise minimization operator using rationality postulates and show representation results for other operators like the Dalal operator.
Prominent approaches to belief revision prescribe the adoption of a new belief that is as close as possible to the prior belief, in a process that, even in the standard case, can be described as attempting to minimize surprise. Here we extend the existing model by proposing a measure of surprise, dubbed relative surprise, in which surprise is computed with respect not just to the prior belief, but also to the broader context provided by the new information, using a measure derived from familiar distance notions between truth-value assignments. We characterize the surprise minimization revision operator thus defined using a set of intuitive rationality postulates in the AGM mould, along the way obtaining representation results for other existing revision operators in the literature, such as the Dalal operator and a recently introduced distance-based min-max operator.