In recent years, multi-agent epistemic planning has received attention from both dynamic logic and planning communities. Existing implementations of multi-agent epistemic planning are based on compilation into classical planning and suffer from various limitations, such as generating only linear plans, restriction to public actions, and incapability to handle disjunctive beliefs. In this paper, we consider centralized multi-agent epistemic planning from the viewpoint of a third person who coordinates all the agents to achieve the goal. We treat contingent planning, resulting in nonlinear plans. We model private actions and hence handle beliefs, formalized with the multi-agent KD45 logic. We handle static propositional common knowledge, which we call constraints. For such planning settings, we propose a general representation framework where the initial knowledge base (KB) and the goal, the preconditions and effects of actions can be arbitrary KD45_n formulas, and the solution is an action tree branching on sensing results. In this framework, the progression of KBs w.r.t. actions is achieved through the operation of belief revision or update on KD45_n formulas, that is, higher-order belief revision or update. To support efficient reasoning and progression, we make use of a normal form for KD45_n called alternating cover disjunctive formulas (ACDFs). We propose reasoning, revision and update algorithms for ACDFs. Based on these algorithms, adapting the PrAO algorithm for contingent planning from the literature, we implemented a multi-agent epistemic planner called MEPK. Our experimental results show the viability of our approach.

近年来，多智能体认知规划受到了动态逻辑和规划社区的关注。多智能体认知规划的现有实现基于编译为经典规划，并受到各种限制，例如仅生成线性规划、对公共行为的限制以及无法处理分离的信念。在本文中，我们从协调所有代理以实现目标的第三人的角度考虑集中式多代理认知规划。我们处理临时计划，导致计划非线性。我们对私人行为进行建模，从而处理信念，用多代理 KD45 逻辑形式化。我们处理静态命题常识，我们称之为约束。对于这样的规划设置，_n个公式，解决方案是在感知结果上分支的动作树。在该框架中，KBs wrt 动作的推进是通过对 KD45 _n公式进行信念修正或更新的操作来实现的，即高阶信念修正或更新。为了支持有效的推理和进展，我们使用 KD45 _n的范式，称为交替覆盖析取公式 (ACDF)。我们提出了 ACDF 的推理、修订和更新算法。基于这些算法，我们将 PrAO 算法应用于文献中的应急规划，我们实现了一个名为 MEPK 的多智能体认知规划器。我们的实验结果表明了我们方法的可行性。