AI RESEARCH
An Abstract Worlds Semantic Framework for Belief Change Operators
arXiv CS.AI
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ArXi:2606.02163v1 Announce Type: new This article proposes a set-theoretic framework for belief change, called Abstract Worlds Semantics, in which no logical syntax is assumed. Inspired by Grove's results, our approach treats worlds as primitive elements, over which world contraction and world revision operators are defined. This semantic framework enables a unified analysis of belief change models. Within this framework, we unify classical and non-prioritized belief change constructions by defining versatile operators.