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Latency-Aware 2-Opt Monotonic Local Search for Distributed Constraint Optimization

Authors Ben Rachmut , Roie Zivan , William Yeoh

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Author Details

Ben Rachmut
  • Ben-Gurion University of the Negev, Beersheba, Israel
Roie Zivan
  • Ben-Gurion University of the Negev, Beersheba, Israel
William Yeoh
  • Washington University in St. Louis, MO, USA

Funding

This research is partially supported by US-Israel Binational Science Foundation (BSF) grant #2018081.

Cite AsGet BibTex

Ben Rachmut, Roie Zivan, and William Yeoh. Latency-Aware 2-Opt Monotonic Local Search for Distributed Constraint Optimization. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 24:1-24:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.CP.2024.24

Abstract

Researchers recently extended Distributed Constraint Optimization Problems (DCOPs) to Communication-Aware DCOPs so that they are applicable in scenarios in which messages can be arbitrarily delayed. Distributed asynchronous local search and inference algorithms designed for CA-DCOPs are less vulnerable to message latency than their counterparts for regular DCOPs. However, unlike local search algorithms for (regular) DCOPs that converge to k-opt solutions (with k > 1), that is, they converge to solutions that cannot be improved by a group of k agents), local search CA-DCOP algorithms are limited to 1-opt solutions only. In this paper, we introduce Latency-Aware Monotonic Distributed Local Search-2 (LAMDLS-2), where agents form pairs and coordinate bilateral assignment replacements. LAMDLS-2 is monotonic, converges to a 2-opt solution, and is also robust to message latency, making it suitable for CA-DCOPs. Our results indicate that LAMDLS-2 converges faster than MGM-2, a benchmark algorithm, to a similar 2-opt solution, in various message latency scenarios.

Subject Classification

ACM Subject Classification
  • Theory of computation → Distributed algorithms
Keywords
  • Distributed Constraint Optimization Problems
  • Distributed Local Search Algorithms
  • Latency Awareness
  • Multi-Agent Optimization

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References

  1. Albert-László Barabási and Réka Albert. Emergence of scaling in random networks. Science, 286(5439):509-512, 1999. Google Scholar
  2. Leonid Barenboim and Michael Elkin. Combinatorial algorithms for distributed graph coloring. Distributed Computing, 27(2):79-93, 2014. Google Scholar
  3. Muhammed Basharu, Ines Arana, and Hatem Ahriz. Solving DisCSPs with penalty driven search. In Proceedings of AAAI, pages 47-52, 2005. Google Scholar
  4. Alessandro Farinelli, Alex Rogers, Adrian Petcu, and Nicholas R. Jennings. Decentralised coordination of low-power embedded devices using the Max-Sum algorithm. In Proceedings of AAMAS, pages 639-646, 2008. Google Scholar
  5. Ferdinando Fioretto, Enrico Pontelli, and William Yeoh. Distributed constraint optimization problems and applications: A survey. Journal of Artificial Intelligence Research, 61:623-698, 2018. Google Scholar
  6. Ferdinando Fioretto, William Yeoh, and Enrico Pontelli. A multiagent system approach to scheduling devices in smart homes. In Proceedings of AAMAS, pages 981-989, 2017. Google Scholar
  7. Tal Grinshpoun, Tamir Tassa, Vadim Levit, and Roie Zivan. Privacy preserving region optimal algorithms for symmetric and asymmetric DCOPs. Artificial Intelligence, 266:27-50, 2019. Google Scholar
  8. Khoi D. Hoang, Ferdinando Fioretto, William Yeoh, Enrico Pontelli, and Roie Zivan. A large neighboring search schema for multi-agent optimization. In Proceedings of CP, pages 688-706, 2018. Google Scholar
  9. Christopher Kiekintveld, Zhengyu Yin, Atul Kumar, and Milind Tambe. Asynchronous algorithms for approximate distributed constraint optimization with quality bounds. In Proceedings of AAMAS, pages 133-140, 2010. Google Scholar
  10. Rajiv T. Maheswaran, Jonathan Pearce, and Milind Tambe. Distributed algorithms for DCOP: A graphical game-based approach. In Proceedings of PDCS, pages 432-439, 2004. Google Scholar
  11. Rajiv T. Maheswaran, Milind Tambe, Emma Bowring, Jonathan P. Pearce, and Pradeep Varakantham. Taking DCOP to the real world: Efficient complete solutions for distributed multi-event scheduling. In Proceedings of AAMAS, 2004. Google Scholar
  12. Pragnesh J. Modi, Wei-Min Shen, Milind Tambe, and Makoto Yokoo. ADOPT: Asynchronous distributed constraint optimization with quality guarantees. Artificial Intelligence, 161(1-2):149-180, 2005. Google Scholar
  13. Arnon Netzer, Alon Grubshtein, and Amnon Meisels. Concurrent forward bounding for distributed constraint optimization problems. Artificial Intelligence, 193:186-216, 2012. Google Scholar
  14. Duc Thien Nguyen, William Yeoh, Hoong Chuin Lau, and Roie Zivan. Distributed Gibbs: A linear-space sampling-based DCOP algorithm. Journal of Artificial Intelligence Research, 64:705-748, 2019. Google Scholar
  15. Jonathan Pearce and Milind Tambe. Quality guarantees on k-optimal solutions for distributed constraint optimization problems. In Proceedings of IJCAI, pages 1446-1451, 2007. Google Scholar
  16. Adrian Petcu and Boi Faltings. A scalable method for multiagent constraint optimization. In Proceedings of IJCAI, pages 1413-1420, 2005. Google Scholar
  17. Ben Rachmut, Roie Zivan, and William Yeoh. Latency-aware local search for distributed constraint optimization. In Proceedings of AAMAS, pages 1019-1027, 2021. Google Scholar
  18. Ben Rachmut, Roie Zivan, and William Yeoh. Communication-aware local search for distributed constraint optimization. Journal of Artificial Intelligence Research, 75:637-675, 2022. Google Scholar
  19. Pierre Rust, Gauthier Picard, and Fano Ramparany. Resilient distributed constraint reasoning to autonomously configure and adapt IoT environments. ACM Transactions on Internet Technology, 22(4):1-31, 2022. Google Scholar
  20. Melanie Smith and Roger Mailler. Getting what you pay for: Is exploration in distributed hill climbing really worth it? In Proceedings of IAT, pages 319-326, 2010. Google Scholar
  21. Meritxell Vinyals, Eric Anyung Shieh, Jesús Cerquides, Juan A. Rodríguez-Aguilar, Zhengyu Yin, Milind Tambe, and Emma Bowring. Quality guarantees for region optimal DCOP algorithms. In Proceedings of AAMAS, pages 133-140, 2011. Google Scholar
  22. William Yeoh, Ariel Felner, and Sven Koenig. BnB-ADOPT: An asynchronous branch-and-bound DCOP algorithm. Journal of Artificial Intelligence Research, 38:85-133, 2010. Google Scholar
  23. Makoto Yokoo and Katsutoshi Hirayama. Distributed breakout algorithm for solving distributed constraint satisfaction problems. In Proceedings of AAMAS, pages 401-408, 1996. Google Scholar
  24. Weixiong Zhang, Guandong Wang, Zhao Xing, and Lars Wittenburg. Distributed stochastic search and distributed breakout: properties, comparison and applications to constraint optimization problems in sensor networks. Artificial Intelligence, 161(1-2):55-87, 2005. Google Scholar
  25. Roie Zivan and Amnon Meisels. Message delay and DisCSP search algorithms. Annals of Mathematics and Artificial Intelligence, 46:415-439, 2006. Google Scholar
  26. Roie Zivan, Steven Okamoto, and Hilla Peled. Explorative anytime local search for distributed constraint optimization. Artificial Intelligence, 212:1-26, 2014. Google Scholar
  27. Roie Zivan, Ben Rachmut, Omer Perry, and William Yeoh. Effect of asynchronous execution and imperfect communication on max-sum belief propagation. Autonomous Agents and Multi-Agent Systems, 37(2), 2023. Google Scholar
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