Title:
Learning task performance in market-based task allocation
Learning task performance in market-based task allocation
dc.contributor.author | Pippin, Charles, E. | en_US |
dc.contributor.author | Christensen, Henrik I. | en_US |
dc.contributor.corporatename | Georgia Tech Research Institute | en_US |
dc.contributor.corporatename | Georgia Institute of Technology. College of Computing | en_US |
dc.contributor.corporatename | Georgia Institute of Technology. Center for Robotics and Intelligent Machines | en_US |
dc.date.accessioned | 2013-02-14T20:48:26Z | |
dc.date.available | 2013-02-14T20:48:26Z | |
dc.date.issued | 2012-06 | |
dc.description | © 2012 Springer-Verlag. The original publication is available at www.springerlink.com. | en_US |
dc.description | Presented at the 12th International Conference on Intelligent Autonomous Systems (IAS-12) held June 26-29, 2012, Jeju Island, Korea. | en_US |
dc.description | DOI: 10.1007/978-3-642-33932-5_57 | en_US |
dc.description.abstract | Auction based algorithms offer effective methods for de-centralized task assignment in multi-agent teams. Typically there is an implicit assumption that agents can be trusted to effectively perform assigned tasks. However, reliable performance of team members may not always be a valid assumption. An approach to learning team member performance is presented, which enables more efficient task assignment. A policy gradient reinforcement learning algorithm is used to learn a cost factor that can be applied individually to auction bids. Experimental results demonstrate that agents that model team member performance using this approach can more efficiently distribute tasks in multi-agent auctions. | en_US |
dc.identifier.citation | Pippin, C., and Christensen, H. I., “Learning task performance in market-based task allocation,” Proceedings of the 12th International Conference on Intelligent Autonomous Systems, IAS-12, June 26-29, 2012, Jeju Island, Korea, 613-621. | en_US |
dc.identifier.doi | 10.1007/978-3-642-33932-5_57 | |
dc.identifier.isbn | 978-3-642-33931-8 | |
dc.identifier.issn | 2194-5357 | |
dc.identifier.uri | http://hdl.handle.net/1853/46195 | |
dc.language.iso | en_US | en_US |
dc.publisher | Georgia Institute of Technology | en_US |
dc.publisher.original | Springer-Verlag | en_US |
dc.subject | Computational intelligence | en_US |
dc.subject | Auction based algorithms | en_US |
dc.subject | Market-based task allocation | en_US |
dc.subject | Policy gradient reinforcement learning | en_US |
dc.title | Learning task performance in market-based task allocation | en_US |
dc.type | Text | |
dc.type.genre | Proceedings | |
dc.type.genre | Post-print | |
dspace.entity.type | Publication | |
local.contributor.author | Christensen, Henrik I. | |
local.contributor.corporatename | Institute for Robotics and Intelligent Machines (IRIM) | |
relation.isAuthorOfPublication | afdc727f-2705-4744-945f-e7d414f2212b | |
relation.isOrgUnitOfPublication | 66259949-abfd-45c2-9dcc-5a6f2c013bcf |
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