Scalable greedy algorithms for task/resource constrained multi-agent stochastic planning
Synergistic interactions between task/resource allocation and stochastic planning exist in many environments such as transportation and logistics, UAV task assignment and disaster rescue. Existing research in exploiting these synergistic interactions between the two problems have either only conside...
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sg-smu-ink.sis_research-46012020-03-24T06:11:23Z Scalable greedy algorithms for task/resource constrained multi-agent stochastic planning AGRAWAL, Pritee Pradeep VARAKANTHAM, YEOH, William Synergistic interactions between task/resource allocation and stochastic planning exist in many environments such as transportation and logistics, UAV task assignment and disaster rescue. Existing research in exploiting these synergistic interactions between the two problems have either only considered domains where tasks/resources are completely independent of each other or have focussed on approaches with limited scalability. In this paper, we address these two limitations by introducing a generic model for task/resource constrained multi-agent stochastic planning, referred to as TasC-MDPs. We provide two scalable greedy algorithms, one of which provides posterior quality guarantees. Finally, we illustrate the high scalability and solution performance of our approaches in comparison with existing work on two benchmark problems from the literature. 2016-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3600 https://ink.library.smu.edu.sg/context/sis_research/article/4601/viewcontent/Scalable_greedy_algorithms_for_task.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 Markov Decision Problems Multi-Agent Planning Reasoning with Uncertainty Artificial Intelligence and Robotics Theory and Algorithms |
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Markov Decision Problems Multi-Agent Planning Reasoning with Uncertainty Artificial Intelligence and Robotics Theory and Algorithms AGRAWAL, Pritee Pradeep VARAKANTHAM, YEOH, William Scalable greedy algorithms for task/resource constrained multi-agent stochastic planning |
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Synergistic interactions between task/resource allocation and stochastic planning exist in many environments such as transportation and logistics, UAV task assignment and disaster rescue. Existing research in exploiting these synergistic interactions between the two problems have either only considered domains where tasks/resources are completely independent of each other or have focussed on approaches with limited scalability. In this paper, we address these two limitations by introducing a generic model for task/resource constrained multi-agent stochastic planning, referred to as TasC-MDPs. We provide two scalable greedy algorithms, one of which provides posterior quality guarantees. Finally, we illustrate the high scalability and solution performance of our approaches in comparison with existing work on two benchmark problems from the literature. |
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AGRAWAL, Pritee Pradeep VARAKANTHAM, YEOH, William |
author_facet |
AGRAWAL, Pritee Pradeep VARAKANTHAM, YEOH, William |
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AGRAWAL, Pritee |
title |
Scalable greedy algorithms for task/resource constrained multi-agent stochastic planning |
title_short |
Scalable greedy algorithms for task/resource constrained multi-agent stochastic planning |
title_full |
Scalable greedy algorithms for task/resource constrained multi-agent stochastic planning |
title_fullStr |
Scalable greedy algorithms for task/resource constrained multi-agent stochastic planning |
title_full_unstemmed |
Scalable greedy algorithms for task/resource constrained multi-agent stochastic planning |
title_sort |
scalable greedy algorithms for task/resource constrained multi-agent stochastic planning |
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Institutional Knowledge at Singapore Management University |
publishDate |
2016 |
url |
https://ink.library.smu.edu.sg/sis_research/3600 https://ink.library.smu.edu.sg/context/sis_research/article/4601/viewcontent/Scalable_greedy_algorithms_for_task.pdf |
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