Jorge Cortés


Dynamic domain reduction for multi-agent planning
A. Ma, M. Ouimet, and J. Cortés
International Symposium on Multi-Robot and Multi-Agent Systems, Los Angeles, California, 2017, pp. 142-149


We consider a scenario where a swarm of arbitrary unmanned vehicles (UxVs) are used to spatially satisfy a multitude of diverse objectives. The UxVs strive to determine an efficient schedule of tasks to service the objectives while operating as a swarm. We focus on developing autonomous high-level planning, where low-level controls are leveraged from previous work in distributed motion, target tracking, localization, and communication algorithms. We take a Markov decision processes (MDP) approach to develop a multi-agent framework that can extend to multi-objective optimization and human-interaction for swarm robotics. Utilizing state and action abstractions, we introduce a hierarchical algorithm, Dynamic domain reduction for multi-agent planning, to enable multi-agent planning for large multi-objective environments. Simulated results show significant improvement over using a standard Monte carlo tree search in an environment with massive state and action spaces.


Mechanical and Aerospace Engineering, University of California, San Diego
9500 Gilman Dr, La Jolla, California, 92093-0411

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