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

点击翻译按钮获取中文摘要

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.