CRII: RI: Planning and learning with macro-actions in cooperative multiagent systems

该提案旨在研究分散规划的问题。一般的技术领域是分散的部分可观察马尔可夫决策过程（Dec-POMDP）。PI通过使用Dec-POMDPs的有限状态控制器提出了一种关于宏动作的理论。宏动作使规划器能够在单个计算周期中执行多个规划步骤，而使用常规动作的规划器在每个计算周期中只能执行一个动作。因此，宏观行动有可能更有效地解决规划问题，从而实现（1）跨多个代理的分布式规划任务，（2）在代理对世界状态只有有限知识的环境中进行规划，以及（3）在不确定的环境中，行动可能会有多个结果的规划。有许多潜在的应用领域，这项研究，包括分布式代理监测一个用于安全漏洞、分布式军事规划和协调参与灾难恢复任务的多个机器人的网络。

The proposal aims at studying the problem of decentralized planning. The general technical area is decentralized partially observable Markov decision process (Dec-POMDP). The PI proposes a theory on macro-actions by using finite-state controllers of Dec-POMDPs. Macro-actions enable the planner to perform multiple planning steps in a single computation cycle whereas a planner using regular actions can only perform one action in each computation cycles. As a result, macro-actions have the potential to solve planning problems much more efficiently enabling (1) distributed planning tasks across multiple agents, (2) planning in environments where agents have only limited knowledge about the state of the world, and (3) planning in uncertain environments where actions might have multiple outcomes.There are many potential areas of application of this research including distributed agents monitoring a network for security breaches, distributed military planning, and coordinating multiple robots involved in disaster recovery tasks.