arXiv AI

An Abstract Worlds Semantic Framework for Belief Change Operators

arXiv:2606. 02163v1 Announce Type: new Abstract: This article proposes a set-theoretic framework for belief change, called Abstract Worlds Semantics, in which no logical syntax is assumed.

arXiv AI
Jul 1

Belief Contraction in Dynamic Epistemic Logic

arXiv:2606. 31861v1 Announce Type: cross Abstract: Dynamic epistemic logic represents belief change via model transformations induced by epistemic events.

By Gaia Belardinelli (Stanford University), Snow Zhang (University of Berkeley, California)
arXiv AI
Jul 24

Explainable Belief Harmonization under Dynamic Epistemic Partitions

arXiv:2607. 21210v1 Announce Type: cross Abstract: Existing approaches to multi-agent belief combination have established mature foundations for combining uncertain beliefs under common assumptions: consensus methods use iterative averaging, logic-based methods resolve conflicting knowledge bases, and epistemic logic analyzes agents' information states.

By Adam Kostka (Warsaw University of Technology), Jaros{\l}aw A. Chudziak (Warsaw University of Technology)
arXiv AI
Sep 4

Semantic Bayesian World Models

Semantic Bayesian World Models (SBWMs) propose a shift from static knowledge graphs to a dynamic, probabilistic fabric of beliefs that can be updated via Bayesian conditioning and influenced by actions. The approach aims to bridge the gap between crisp factual assertions and the probabilistic reasoning of foundation models and autonomous agents, enabling richer inference in scenarios such as home‑security decisions, actuarial estimates, and planning tasks. Realizing SBWMs requires new tools for belief annotation, probabilistic entailment, semantic calibration, and protocols for belief exchange among agents.

By Tommaso Soru
arXiv AI
Jun 9

Standpoint Logics with Defeasible Beliefs

arXiv:2606. 08503v1 Announce Type: new Abstract: In this paper, we integrate the defeasible logic of Kraus, Lehmann and Magidor (KLM) with the standpoint logic framework of G\'omez \'Alvarez and Rudolph.

By Nicholas Leisegang, Thomas Meyer, Sebastian Rudolph
arXiv AI
Sep 10

Evidential-Based Higher-Order Set Argumentation Framework

The paper introduces the Evidential-Based Higher-Order Set Argumentation Framework (EHSAF), a unified formalism that extends Dung’s abstract argumentation by incorporating evidential support, higher-order relations, and collective interactions. Two complete semantics are defined: an adjacent complete labelling semantics allowing multiple truth values for arguments in support cycles, and an extension-based complete semantics that accepts only well‑founded support chains. The authors provide a propositional encoding in three‑valued Łukasiewicz logic and extend it to continuous fuzzy logics, proving key properties and showing equivalence under support‑acyclicity.

By Shuai Tang