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Modelling and Data Analysis
2019. Vol. 9, no. 4, 100–111
doi:10.17759/mda.2019090408
ISSN: 2219-3758 / 2311-9454 (online)

Multi-Agent Modeling in Schedule Problems

182

V.A. Sudakov, T.V. Sivakova

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Abstract

The article explores the use of multi-agent technologies for solving optimization problems. It is shown how multi-agent systems allow working with restrictions in a distributed computing environment. The task of scheduling is formalized. Software was developed and computational experiments were carried out, which showed the effectiveness of the proposed approach.

General Information

Keywords: multiagent systems, agent preferences, optimization, distributed systems

Journal rubric: Optimization Methods

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2019090408

Funding. This work was supported by grant RFBR No 18–00–00012 (18–00–00011) KOMFI.

For citation: Sudakov V.A., Sivakova T.V. Multi-Agent Modeling in Schedule Problems. Modelirovanie i analiz dannikh = Modelling and Data Analysis, 2019. Vol. 9, no. 4, pp. 100–111. DOI: 10.17759/mda.2019090408. (In Russ., аbstr. in Engl.)

References

  1. A. Farinelli, M. Vinyals, A. Rogers, and N. Jennings. “Distributed Constraint Handling and Optimization”, in G. Weiss (ed.), “Multiagent Systems” (second edition), MIT Press, p. 547–584, 2013.
  2. Panteleev A.V., Metlitskaya D.V., Aleshina E.A. Metody global’noi optimizatsii. Metaevristicheskie strategii i algoritmy [Global optimization methods, Metaheuristic strategies and algorithms]. Moscow, Vuzovskayakniga, 2013. 244 p. (in Russian)
  3. Sivakova T.V., Sudakov V.A. Metod nechetkih oblastej predpochtenii dlya ocenki effektivnosti innovacij [Fuzzy preference method for evaluating innovation performance] // XXVIII Mezhdunarodnaya nauchno-tekhnicheskaya konferenciya «Sovremennye tekhnologii v zadachah upravleniya, avtomatiki i obrabotki informacii». Alushta, 14–20 sentyabrya 2019 g. Sbornik trudov. M.: Izd.-vo Nacional’nyj issledovatel’skij yadernyj universitet “MIFI”, 2019. 81–82. (in Russian)
  4. R. Dechter. Constraint Processing. Morgan Kaufmann, 2003.
  5. Makoto Yokoo. Distributed constraint satisfaction: Foundations of cooperation in multiagent systems. Springer-Verlag, 2001.
  6. P.J. Modi, W. Shen, M. Tambe, and M. Yokoo. ADOPT: Asynchronous distributed constraint optimization with quality guarantees. Artificial Intelligence Journal, (161):149–180, 2005.
  7. A. Chechetka and K. Sycara. No-commitment branch and bound search for distributed constraint optimization. In Proceedings of Fifth International Joint Confer- ence on Autonomous Agents and Multiagent Systems, pages 1427–1429, 2006.
  8. Gershman, A. Meisels, and R. Zivan. Asynchronous forward bounding for dis- tribute COPs. Journal Artifi cial Intelligence Research, 34:61–88, 2009.
  9. R. Dechter and R. Mateescu. And/or search spaces for graphical models. Artificial Intelligence, 171:73–106, 2007.
  10. Katsutoshi Hirayama and Makoto Yokoo. Distributed partial constraint satisfaction problem. In Principles and Practice of Constraint Programming, pages 222–236, 1997.
  11. A. Petcu and B. Faltings. DPOP: A scalable method for multiagent constraint optimization. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, pages 266–271, 2005.
  12. R.Maillerand, V.Lesser. Solving distributed constraint optimization problems using cooperative mediation. In Proceedings of Third International Joint Conference on Autonomous Agents and MultiAgent Systems, pages 438–445, 2004.
  13. Library for research on Distributed Constraints Optimization Problems. URL: https://github.com/Orange-OpenSource/pyDcop (26.10.2019)
  14. W. Yeoh, A. Felner, and S. Koenig. BnB-ADOPT: An asynchronous branch-and- bound DCOP algorithm. In Proceedings of the Seventh International Joint Conference on Autonomous Agents and Multiagent Systems, pages 591–598, 2008.
  15. S.M. Ali, S. Koenig, and M. Tambe. Preprocessing techniques for accelerating the DCOP algorithm ADOPT. In Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, pages 1041–1048, 2005.

Information About the Authors

Vladimir A. Sudakov, Doctor of Engineering, Professor of Department 805, Moscow Aviation Institute (MAI), Leading Researcher, Keldysh Institute of Applied Mathematics (Russian Academy of Sciences), Moscow, Russia, ORCID: https://orcid.org/0000-0002-1658-1941, e-mail: sudakov@ws-dss.com

Tatyana V. Sivakova, Researcher, Keldysh Institute of Applied Mathematics (Russian Academy of Sciences), researcher, Plekhanov Russian University of Economics, Moscow, Russia, ORCID: https://orcid.org/0000-0001-8026-2198, e-mail: sivakova15@mail.ru

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