Multi-Agent Modeling in Schedule Problems 102
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.
This work was supported by grant RFBR No 18–00–00012 (18–00–00011) KOMFI.
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.
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)
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)
R. Dechter. Constraint Processing. Morgan Kaufmann,
Makoto Yokoo. Distributed constraint satisfaction:
Foundations of cooperation in multiagent systems. Springer-Verlag, 2001.
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.
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.
Gershman, A. Meisels, and R. Zivan. Asynchronous forward
bounding for dis- tribute COPs. Journal Artifi cial Intelligence Research,
R. Dechter and R. Mateescu. And/or search spaces for
graphical models. Artificial Intelligence, 171:73–106, 2007.
Katsutoshi Hirayama and Makoto Yokoo. Distributed partial
constraint satisfaction problem. In Principles and Practice of Constraint
Programming, pages 222–236, 1997.
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,
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.
Library for research on Distributed Constraints
Optimization Problems. URL: https://github.com/Orange-OpenSource/pyDcop
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.
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.