Machine Learning for Efficient Sampling-Based Algorithms in Robust Multi-Agent Planning Under Uncertainty
Robust multi-agent planning algorithms have been developed to assign tasks to cooperative teams of robots operating under various uncertainties. Often, it is difficult to evaluate the robustness of potential task assignments analytically, so sampling-based approximations are used instead. In many ap...
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American Institute of Aeronautics and Astronautics (AIAA)
2018
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| Online Access: | http://hdl.handle.net/1721.1/114295 https://orcid.org/0000-0002-0464-4108 https://orcid.org/0000-0001-8576-1930 |