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

CRII：RI：协作多智能体系统中宏观行动的规划和学习

• 批准号: 1463945
• 负责人: Christopher Amato
• 金额: $ 17.48万
• 依托单位: University of New Hampshire
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-01 至 2016-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1463945&HistoricalAwards=false
• 关键词: CRII; RI; Planning; learning; macro

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.

该提案旨在研究分散规划的问题。通用技术领域是去中心化部分可观察马尔可夫决策过程（Dec-POMDP）。 PI 通过使用 Dec-POMDP 的有限状态控制器提出了宏观行为理论。宏操作使规划器能够在单个计算周期中执行多个规划步骤，而使用常规操作的规划器只能在每个计算周期中执行一个操作。因此，宏观行动有可能更有效地解决规划问题，从而实现 (1) 跨多个智能体的分布式规划任务，(2) 在智能体对世界状况了解有限的环境中进行规划，以及 (3 ）在行动可能产生多种结果的不确定环境中进行规划。这项研究有许多潜在的应用领域，包括监控网络安全漏洞的分布式代理、分布式军事规划以及协调参与灾难恢复任务的多个机器人。