Prioritized Shaping of Models for Solving DEC-POMDPs
An interesting class of multi-agent POMDP planning problems can be solved by having agents iteratively solve individual POMDPs, find interactions with other individual plans, shape their transition and reward functions to encourage good interactions and discourage bad ones and then recompute a new p...
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sg-smu-ink.sis_research-26062018-07-13T03:10:28Z Prioritized Shaping of Models for Solving DEC-POMDPs VARAKANTHAM, Pradeep Reddy YEOH, William Velagapudi, Prasanna Scerri, Paul An interesting class of multi-agent POMDP planning problems can be solved by having agents iteratively solve individual POMDPs, find interactions with other individual plans, shape their transition and reward functions to encourage good interactions and discourage bad ones and then recompute a new plan. D-TREMOR showed that this approach can allow distributed planning for hundreds of agents. However, the quality and speed of the planning process depends on the prioritization scheme used. Lower priority agents shape their models with respect to the models of higher priority agents. In this paper, we introduce a new prioritization scheme that is guaranteed to converge and is empirically better, in terms of solution quality and planning time, than the existing prioritization scheme for some problems. 2012-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/1607 https://ink.library.smu.edu.sg/context/sis_research/article/2606/viewcontent/C14___Prioritized_Shaping_of_Models_for_Solving_DEC_POMDPs__AAMAS2012_.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University DEC-POMDP Uncertainty Multi-Agent Systems Artificial Intelligence and Robotics Business Operations Research, Systems Engineering and Industrial Engineering |
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DEC-POMDP Uncertainty Multi-Agent Systems Artificial Intelligence and Robotics Business Operations Research, Systems Engineering and Industrial Engineering VARAKANTHAM, Pradeep Reddy YEOH, William Velagapudi, Prasanna Scerri, Paul Prioritized Shaping of Models for Solving DEC-POMDPs |
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An interesting class of multi-agent POMDP planning problems can be solved by having agents iteratively solve individual POMDPs, find interactions with other individual plans, shape their transition and reward functions to encourage good interactions and discourage bad ones and then recompute a new plan. D-TREMOR showed that this approach can allow distributed planning for hundreds of agents. However, the quality and speed of the planning process depends on the prioritization scheme used. Lower priority agents shape their models with respect to the models of higher priority agents. In this paper, we introduce a new prioritization scheme that is guaranteed to converge and is empirically better, in terms of solution quality and planning time, than the existing prioritization scheme for some problems. |
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VARAKANTHAM, Pradeep Reddy YEOH, William Velagapudi, Prasanna Scerri, Paul |
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VARAKANTHAM, Pradeep Reddy YEOH, William Velagapudi, Prasanna Scerri, Paul |
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VARAKANTHAM, Pradeep Reddy |
title |
Prioritized Shaping of Models for Solving DEC-POMDPs |
title_short |
Prioritized Shaping of Models for Solving DEC-POMDPs |
title_full |
Prioritized Shaping of Models for Solving DEC-POMDPs |
title_fullStr |
Prioritized Shaping of Models for Solving DEC-POMDPs |
title_full_unstemmed |
Prioritized Shaping of Models for Solving DEC-POMDPs |
title_sort |
prioritized shaping of models for solving dec-pomdps |
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Institutional Knowledge at Singapore Management University |
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2012 |
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https://ink.library.smu.edu.sg/sis_research/1607 https://ink.library.smu.edu.sg/context/sis_research/article/2606/viewcontent/C14___Prioritized_Shaping_of_Models_for_Solving_DEC_POMDPs__AAMAS2012_.pdf |
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1770571347851739136 |