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DCOPolis: a framework for simulating and deploying distributed constraint reasoning algorithms

Published: 12 May 2008 Publication History

Abstract

The proliferation of mobile computers---such as laptops, personal digital assistants, and smart phones---has propelled distributed computing into mainstream society. Over the past decade these technologies have spurred interest in both decentralized multiagent systems and wireless mobile ad-hoc networks. Such networks, however, present many challenges to information sharing and coordination. Interference, obstacles, and other environmental effects conspire with power-and processing-limited hardware to impose a number of challenging networking characteristics. Messages are routinely lost or delayed, connections may be only sporadically available, and network transfer capacity is nowhere near that available on modern wired networks. It is therefore imperative to emphasize local decision making and autonomy over a centralized analogue, insofar as it is possible. The majority of such decentralized decision making can be seen as a fundamental problem of propagating and then solving systems of constraints, otherwise known as Distributed Constraint Reasoning (DCR).

References

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J. Davin and P. J. Modi. Impact of problem centralization in distributed constraint optimization algorithms. In Proceedings of AAMAS, pages 1057--1063, 2005.
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A. Haeberlen, A. Mislove, A. Post, and P. Druschel. Fallacies in evaluating decentralized systems. In Proceedings of the Fifth International Workshop on Peer-to-Peer Systems, February 2006.
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R. Mailler and V. Lesser. Solving distributed constraint optimization problems using cooperative mediation. In Proceedings of AAMAS, pages 438--445, 2004.
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A. Meisels, E. Kaplansky, I. Razgon, and R. Zivan. Comparing performance of distributed constraints processing algorithms. In Proceedings of the Third International Workshop on DCR, 2002.
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P. J. Modi, W.-M. Shen, M. Tambe, and M. Yokoo. An asynchronous complete method for distributed constraint optimization. In Proceedings of AAMAS, pages 161--168, 2003.
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A. Petcu and B. Faltings. A distributed, complete method for multi-agent constraint optimization. In Proceedings of the Fifth International Workshop on DCR, September 2004.
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E. Sultanik, R. Lass, and W. Regli. DCOPolis: A framework for simulating and deploying distributed constraint optimization algorithms. In Proceedings of the Ninth International Workshop on DCR, Providence, RI, September 2007.
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Cited By

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  • (2019)A privacy preserving collusion secure DCOP algorithmProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367471.3367707(4774-4780)Online publication date: 10-Aug-2019
  • (2018)A Near-Optimal Node-to-Agent Mapping Heuristic for GDL-Based DCOP Algorithms in Multi-Agent SystemsProceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3237383.3237938(1604-1612)Online publication date: 9-Jul-2018
  • (2017)Privacy preserving implementation of the max-sum algorithm and its variantsJournal of Artificial Intelligence Research10.5555/3176788.317679659:1(311-349)Online publication date: 1-May-2017
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  1. DCOPolis: a framework for simulating and deploying distributed constraint reasoning algorithms

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      cover image ACM Conferences
      AAMAS '08: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: demo papers
      May 2008
      116 pages

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      International Foundation for Autonomous Agents and Multiagent Systems

      Richland, SC

      Publication History

      Published: 12 May 2008

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      Author Tags

      1. distributed constraint reasoning
      2. middleware
      3. simulation

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

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      View all
      • (2019)A privacy preserving collusion secure DCOP algorithmProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367471.3367707(4774-4780)Online publication date: 10-Aug-2019
      • (2018)A Near-Optimal Node-to-Agent Mapping Heuristic for GDL-Based DCOP Algorithms in Multi-Agent SystemsProceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3237383.3237938(1604-1612)Online publication date: 9-Jul-2018
      • (2017)Privacy preserving implementation of the max-sum algorithm and its variantsJournal of Artificial Intelligence Research10.5555/3176788.317679659:1(311-349)Online publication date: 1-May-2017
      • (2016)P-syncBBJournal of Artificial Intelligence Research10.5555/3176748.317676257:1(621-660)Online publication date: 1-Sep-2016
      • (2016)Preserving privacy in region optimal DCOP algorithmsProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3060621.3060691(496-502)Online publication date: 9-Jul-2016
      • (2016)CuiltProceedings of the Twenty-second European Conference on Artificial Intelligence10.3233/978-1-61499-672-9-1660(1660-1661)Online publication date: 29-Aug-2016
      • (2015)Max-sum goes privateProceedings of the 24th International Conference on Artificial Intelligence10.5555/2832249.2832308(425-431)Online publication date: 25-Jul-2015
      • (2008)Measurement techniques for multiagent systemsProceedings of the 8th Workshop on Performance Metrics for Intelligent Systems10.1145/1774674.1774696(135-142)Online publication date: 19-Aug-2008

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