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This paper introduces an approximation algorithm for stochastic multi-agent planning based on Markov decision processes (MDPs). Specifically, we focus on a decentralized approach for planning the actions of a team of cooperating agents with uncertainties in fuel consumption and health-related models. The core idea behind the algorithm presented in this paper is to allow each agent to approximate the...
This paper extends prior work on the persistent mission problem where real-time changes in agent capability are included in the problem formulation. Here, we couple the mission planner with a low-level adaptive controller in real-time to: (1) Provide robustness against actuator degradations and (2) Use parameters internal to the adaptive controller to provide valuable insight into the physical capabilities...
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