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

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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.