发表机构
Warsaw University of Technology（华沙技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究多智能体信念组合中认知分区的运行时变化，提出结合回答集编程与Python的混合方法框架，能处理异构分辨率级别，保证可接受性、修复及解释完整性，经评估可实现完全违规检测和解释覆盖。
AI 中文摘要
现有的多智能体信念组合方法在共同假设下为组合不确定信念建立了成熟基础：共识方法使用迭代平均，基于逻辑的方法解决冲突知识库，认知逻辑分析智能体信息状态。通常，这些方法假定决定每个智能体可表示内容的结构保持固定。然而，在许多场景中，智能体在执行过程中会获得或失去观察能力，曾经可行的可能在结构上变得不可行。本文提出一个形式框架来处理连续信念配置上认知分区的此类运行时变化。一种混合方法利用回答集编程在细化容忍、声明性完整性约束和解释方面的优势以及Python的数值灵活性。该框架适用于智能体在异构且可能变化的分辨率级别上运行的领域，并在细化下提供可接受性保持、在粗化下提供唯一的质量保持修复以及解释完整性的形式保证。对100个随机生成的拓扑变化进行评估证实了完全违规检测和解释覆盖。
英文摘要
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.
CommentsIn Proceedings ICLP 2026, arXiv:2607.17707
Journal refEPTCS 450, 2026, pp. 529-543
