动态调整认知分区下的信念融合，确保推理可靠且可解释。
Explainable Belief Harmonization under Dynamic Epistemic Partitions
- 结合ASP与Python，实现认知结构动态变化时的信念协调
- 在100次拓扑变化测试中100%检测违规并完整生成解释
- 适用于观测能力动态变化的异构智能体系统
现有多智能体信念融合方法在固定认知结构假设下已成熟：共识方法采用迭代平均，逻辑方法解决知识库冲突，认知逻辑分析信息状态。然而，在许多场景中，智能体执行过程中可能获得或失去观测能力，导致原本可接受的信念变得结构上不可能。本文提出一个形式化框架，用于处理连续信念分布下认知划分的运行时变化。该框架采用混合方法，结合答案集编程（ASP）在抗扩展性、声明式完整性约束和解释生成方面的优势，以及Python的数值灵活性。适用于异构且可能动态变化分辨率的智能体环境，提供形式保证：细化时保持可接受性，粗化时唯一保质量修复，解释完整性。在100次随机生成的拓扑变化评估中，实现了完全违规检测与解释覆盖。
原文摘要 · Abstract (English)
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.
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