Multi-agent Epistemic Planning (MEP) is an autonomous planning framework for reasoning about both the physical world and the beliefs of agents, with applications in domains where information flow and awareness among agents are critical. The richness of MEP requires states to be represented as Kripke structures, i.e., directed labeled graphs. This representation limits the applicability of existing heuristics, hindering the scalability of epistemic solvers, which must explore an exponential search space without guidance, resulting often in intractability. To address this, we exploit Graph Neural Networks (GNNs) to learn patterns and relational structures within epistemic states, to guide the planning process. GNNs, which naturally capture the graph-like nature of Kripke models, allow us to derive meaningful estimates of state quality -- e.g., the distance from the nearest goal -- by generalizing knowledge obtained from previously solved planning instances. We integrate these predictive heuristics into an epistemic planning pipeline and evaluate them against standard baselines, showing improvements in the scalability of multi-agent epistemic planning.

翻译：多智能体认知规划（MEP）是一种能够同时推理物理世界与智能体信念的自主规划框架，在信息流与智能体间认知状态至关重要的领域具有广泛应用。MEP的丰富性要求状态必须表示为克里普克结构（即有向标记图）。这种表示方式限制了现有启发式方法的适用性，阻碍了认知规划求解器的可扩展性——此类求解器必须在无引导的情况下探索指数级增长的搜索空间，往往导致计算不可行。为解决该问题，我们利用图神经网络（GNN）学习认知状态内部的模式与关系结构，以指导规划过程。GNN天然契合克里普克模型的图结构特性，能够通过泛化已求解规划实例所获得的知识，推导出对状态质量（例如与最近目标状态的距离）的有效估计。我们将这些预测性启发式方法整合到认知规划流程中，并与标准基线方法进行对比评估，结果表明该方法显著提升了多智能体认知规划的可扩展性。