Optimal Policies for Routing Multi Sensor-Effector Combined Autonomous Devices

Yura Oh, Warren B. Powell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We use Monte Carlo tree search to create policies that simultaneously perform active learning about the concentration of contaminants using a Bayesian prior, while also performing mitigation. The policy is asymptotically optimal for one device. We then propose different information sharing protocols to coordinate a fleet.

Original languageEnglish (US)
Title of host publicationInternational Symposium on Multi-Robot and Multi-Agent Systems, MRS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages182-184
Number of pages3
ISBN (Electronic)9781728128764
DOIs
StatePublished - Aug 2019
Event2nd International Symposium on Multi-Robot and Multi-Agent Systems, MRS 2019 - New Brunswick, United States
Duration: Aug 22 2019Aug 23 2019

Publication series

NameInternational Symposium on Multi-Robot and Multi-Agent Systems, MRS 2019

Conference

Conference2nd International Symposium on Multi-Robot and Multi-Agent Systems, MRS 2019
CountryUnited States
CityNew Brunswick
Period8/22/198/23/19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Control and Optimization

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  • Cite this

    Oh, Y., & Powell, W. B. (2019). Optimal Policies for Routing Multi Sensor-Effector Combined Autonomous Devices. In International Symposium on Multi-Robot and Multi-Agent Systems, MRS 2019 (pp. 182-184). [8901060] (International Symposium on Multi-Robot and Multi-Agent Systems, MRS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MRS.2019.8901060