Source note2 min read
Explainable Belief Harmonization under Dynamic Epistemic Partitions
Source
arXiv
arxiv.org/abs/2607.21210v1
Source
- Source name: arXiv
- Source kind: paper
- Authors / organization: Adam Kostka, Jarosław A. Chudziak
- Venue: arXiv: cs.LO
- Published: 2026-07-23
- Collected: 2026-07-24
- URL: http://arxiv.org/abs/2607.21210v1
- PDF: https://arxiv.org/pdf/2607.21210v1
- DOI: 10.4204/EPTCS.450.39
Plain-Language Brief
The paper presents a new framework for handling dynamic changes in epistemic partitions (groups of agents with shared knowledge) during execution, where agents might gain or lose observational capacity. The framework combines the advantages of answer set programming and Python to ensure admissibility preservation under refinement, unique mass-preserving repair under coarsening, and explanation completeness.
What the Document Says
- The paper discusses a framework for handling dynamic changes in epistemic partitions over continuous belief profiles.
- It combines answer set programming with Python's numerical flexibility.
- The framework provides guarantees of admissibility preservation under refinement, unique mass-preserving repair under coarsening, and explanation completeness.
- Evaluation shows complete violation detection and explanation coverage across 100 randomly generated topology changes.
Key Concepts Mentioned
- epistemic partitions
- belief profiles
- answer set programming
- Python
Claims Made by the Document
- The framework can handle runtime changes in epistemic partitions.
- It provides guarantees of admissibility preservation under refinement, unique mass-preserving repair under coarsening, and explanation completeness.
Evidence Used
- The text describes a formal framework for handling runtime changes in epistemic partitions.
- It mentions the use of answer set programming and Python's numerical flexibility.
Methods / Approach
UNKNOWN
Limitations Stated by the Authors
UNKNOWN
Limitations Noted by the Agent
Agent interpretation — not from the source.
UNKNOWN
Open Questions
UNKNOWN
Notes for Future Q&A
May include interpretation. Labeled where it does.
UNKNOWN
Raw Abstract / Excerpt
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. Typically, these approaches assume that the structure determining what each agent can represent remains fixed. However, in many scenarios, agents gain or lose observational capacity during execution, and what was once admissible may become structurally impossible. This paper presents a formal framework for handling such runtime changes in epistemic partitions over continuous belief profiles. A hybrid approach exploits the advantages of answer set programming in elaboration tolerance, declarative integrity constraints, and explanations, with the numerical flexibility of Python. The framework applies to domains where agents operate at heterogeneous and possibly changing levels of resolution, and provides formal guarantees of admissibility preservation under refinement, unique mass-preserving repair under coarsening, and explanation completeness. Evaluation across 100 randomly generated topology changes confirms complete violation detection and explanation coverage.
