An Abstract Worlds Semantic Framework for Belief Change Operators
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911741475028992 |
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| author | Grimaldi, Daniel Martinez, M. Vanina Rodriguez, Ricardo O. |
| author_facet | Grimaldi, Daniel Martinez, M. Vanina Rodriguez, Ricardo O. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_02163 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An Abstract Worlds Semantic Framework for Belief Change Operators Grimaldi, Daniel Martinez, M. Vanina Rodriguez, Ricardo O. Artificial Intelligence 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. |
| title | An Abstract Worlds Semantic Framework for Belief Change Operators |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2606.02163 |