RI: Small: A Hybrid Approach For Meta-Level Control Across Agent Boundaries

能够与其环境交互的协作代理的嵌入式系统正变得无处不在。这些系统必须能够根据任务的优先级、资源的可用性、满足这些任务的替代方法的可用性以及未来预期的任务来适应开放环境的动态和不确定特征。该项目正在为多代理元一级控制制定一个框架和支持算法，它将确定何时应进行适应以及应在适应方面投入多少努力，而不是继续执行目前的行动计划。特别是，元级框架将支持协调分散的马尔可夫决策过程，并将这种协调视为从单个代理学习中引导的全局优化问题，反之亦然。该框架将在一个名为NetRads的真实世界的多代理龙卷风跟踪应用程序中进行演示。

Embedded systems of collaborating agents that are capable of interacting with their environments are becoming ubiquitous. These systems must be able to adapt to the dynamic and uncertain characteristics of an open environment based on the priority of tasks, availability of resources, and availability of alternative ways of satisfying these tasks, as well as tasks expected in the future. The project is developing a framework and supporting algorithms for multi-agent meta-level control, which will determine when adaptation should be done and how much effort should be invested in adaptation as opposed to continuing with the current action plan. In particular, the meta-level framework will support coordinating decentralized Markov Decision Processes and views this coordination as a global optimization problem that bootstraps from individual agent learning and vice-versa. The framework will be demonstrated in a real-world multi-agent tornado tracking application called NetRads.