An Abstract Worlds Semantic Framework for Belief Change Operators
For researchers in belief revision, this work systematizes and generalizes existing frameworks, but it is incremental as it builds on Grove's prior work.
The paper proposes Abstract Worlds Semantics (AWS), a set-theoretic framework for belief change that treats worlds as primitives without logical syntax, unifying classical and non-prioritized belief change models. It shows that AWS provides a homogeneous account of AGM, KM, and Multiple Change models when classical propositional logic is considered.
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 (1988) 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. When classical propositional logic is considered, our framework provides a homogeneous account of AGM, KM, and Multiple Change models. In summary, AWS systematizes belief change frameworks and operators, simplifying and generalizing belief change theory over belief sets.