🤖 AI Summary
This work addresses a key limitation in existing multi-agent belief fusion approaches, which typically assume a fixed cognitive partition structure and thus struggle to accommodate dynamic adjustments of the representation space caused by runtime changes in observational capabilities. The paper proposes a formal framework that enables dynamic evolution of cognitive partitions under continuous belief profiles, achieving for the first time interpretable belief reconciliation: refinement preserves admissibility, while coarsening ensures consistency through a unique mass-conserving repair mechanism, accompanied by complete explanations. The framework integrates the declarative constraint reasoning of Answer Set Programming (ASP) with Python’s numerical computation capabilities, leveraging ASP to manage structural changes and generate explanations. Empirical evaluation across 100 randomly generated topological change scenarios demonstrates 100% detection of constraint violations and full explanation coverage, confirming the method’s effectiveness and completeness.
📝 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. 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.