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Sequential resource allocation in multiagent systems with uncertainties

Published: 14 May 2007 Publication History

Abstract

Exchanging scarce resources during execution among a group of agents is one way to improve the overall performance in multiagent systems with limited shared resources, but implementing optimal sequential resource allocation is often a nontrivial problem in complex systems with uncertainties. In this paper, we present an MILP-based algorithm that can automatically break a large mission into multiple phases and make optimal resource (re)allocations at the entry of each phase. We illustrate our algorithms through several increasingly complex classes of sequential resource allocation problems, and show through experiments that our techniques can increase agents' rewards for varying levels of constraints on resources and constraints on exchanging resources.

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Cited By

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  • (2022)A literature review on optimization techniques for adaptation planning in adaptive systemsInformation and Software Technology10.1016/j.infsof.2022.106940149:COnline publication date: 1-Sep-2022
  • (2010)Investigating the robustness of re-scheduling policies with multi-agent system simulationThe International Journal of Advanced Manufacturing Technology10.1007/s00170-010-3049-955:1-4(355-367)Online publication date: 1-Dec-2010
  • (2008)Planning for Coordination and Coordination for PlanningProceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 0110.1109/WIIAT.2008.389(1-3)Online publication date: 9-Dec-2008
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cover image ACM Other conferences
AAMAS '07: Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
May 2007
1585 pages
ISBN:9788190426275
DOI:10.1145/1329125
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 May 2007

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Author Tags

  1. constrained MDPs
  2. mission phasing
  3. mixed integer linear programming
  4. sequential resource allocation

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Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

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Cited By

View all
  • (2022)A literature review on optimization techniques for adaptation planning in adaptive systemsInformation and Software Technology10.1016/j.infsof.2022.106940149:COnline publication date: 1-Sep-2022
  • (2010)Investigating the robustness of re-scheduling policies with multi-agent system simulationThe International Journal of Advanced Manufacturing Technology10.1007/s00170-010-3049-955:1-4(355-367)Online publication date: 1-Dec-2010
  • (2008)Planning for Coordination and Coordination for PlanningProceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 0110.1109/WIIAT.2008.389(1-3)Online publication date: 9-Dec-2008
  • (2007)Norm Emergence in Agent Societies Formed by Dynamically Changing NetworksProceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology10.1109/IAT.2007.76(464-470)Online publication date: 2-Nov-2007

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