Minimal Change in Modal Logic S5

Authors: Carlos Aguilera-Ventura, Jonathan Ben-Naim, Andreas Herzig

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Reproducibility Variable Result LLM Response
Research Type Theoretical We extend belief revision theory from propositional logic to the modal logic S5. Our first contribution takes the form of three new postulates (M1-M3) that go beyond the AGM ones and capture the idea of minimal change in the presence of modalities. Concerning the construction of modal revision operations, we work with set pseudo-distances... Our second contribution is the identification of three axioms (A3-A5)... Our main result states the following: if a pseudo-distance satisfies certain axioms, then the induced revision operation satisfies (M1-M3).
Researcher Affiliation Academia Carlos Aguilera-Ventura*, Jonathan Ben-Naim*, Andreas Herzig* IRIT, CNRS, Univ. Toulouse, France EMAIL, EMAIL, EMAIL
Pseudocode No The paper describes methods using definitions, lemmas, propositions, and proofs in natural language and mathematical notation. It does not contain any structured pseudocode or algorithm blocks.
Open Source Code No The paper does not contain any explicit statements about releasing source code, nor does it provide links to any code repositories or supplementary materials containing code.
Open Datasets No This paper is theoretical research focusing on belief revision in modal logic S5 and does not involve empirical experiments using datasets.
Dataset Splits No This paper is theoretical research and does not involve empirical experiments using datasets, therefore, no dataset splits are mentioned.
Hardware Specification No This paper is theoretical research and does not describe any experimental setup that would require specific hardware specifications.
Software Dependencies No This paper is theoretical research and does not describe any experimental setup that would require specific software dependencies with version numbers.
Experiment Setup No This paper is theoretical research and does not contain details about experimental setup, such as hyperparameters or system-level training settings.