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Abstract
Distributed Multi-Agent Path Finder (DMAPF) is a novel distributed algorithm to solve theMulti-Agent Path Finding (MAPF) problem, where the objective is to find a sequence of movements for agents to reach their assigned locations without colliding with obstacles, which include other agents. The idea ofDMAPF is to decompose a given MAPF problem into smaller sub-problems, then solve them in parallel. It has been shown thatDMAPF can achieve higher scalability compared to centralized methods. This paper addresses two problems in the previous works. First, the previous works only divide problem maps in a simple, rectangular manner. This can create sub-problems with unbalanced numbers of locations in their maps when the shape of the original map is not rectangular or when the obstacles are not uniformly distributed. Having sub-problems that vary in sizes diminishes the effectiveness of parallelism. Second, the idea ofDMAPF is to have agents move across sub-problems until they reach the sub-problems that contain their goals, but the previous works do not have a mechanism to regulate the number of agents residing in the sub-problems, thus it may fail to find the solution when a sub-problem is overcrowded. To mitigate the problems, we introduce (i) a method to decompose MAPF problems with balanced numbers of vertices; and (ii) a mechanism to regulate the number of agents in sub-problems. We also improve the performance of theAnswer Set Programming (ASP) encoding, that was used in previousDMAPF implementations to solve MAPF sub-problem instances, by eliminating unnecessary parameters. The results show that the new solver scales better and is more efficient than the previous versions.
T.C. Son—Was partially supported by NSF grants 1812628, 1914635, and 1757207.
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References
Achá, R.A., López, R., Hagedorn, S., Baier, J.A.: A new boolean encoding for mapf and its performance with asp and maxsat solvers. In: Proceedings of the International Symposium on Combinatorial Search, vol. 12, pp. 11–19 (2021).https://ojs.aaai.org/index.php/SOCS/article/view/18546
Barer, M., Sharon, G., Stern, R., Felner, A.: Suboptimal variants of the conflict-based search algorithm for the multi-agent pathfinding problem. In: Seventh Annual Symposium on Combinatorial Search (2014).https://doi.org/10.3233/978-1-61499-419-0-961
Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T.: Clingo = ASP + control: Preliminary report. In: Technical Communications of the 13th International Conference on Logic Programming, vol. 14(4–5) (2014).https://arxiv.org/abs/1405.3694
Gelfond, M., Lifschitz, V.: Logic programs with classical negation. In: Logic Programming, Proceedings of the Seventh International Conference, pp. 579–597. MIT Press, Jerusalem, Israel (June 1990).https://dl.acm.org/doi/10.5555/87961.88030
Gómez, R.N., Hernández, C., Baier, J.A.: A compact answer set programming encoding of multi-agent pathfinding. IEEE Access9, 26886–26901 (2021).https://doi.org/10.1109/ACCESS.2021.3053547
Kuhn, H.W.: The hungarian method for the assignment problem. Naval Res. Logist. Quart.2(1–2), 83–97 (1955).https://doi.org/10.1002/nav.3800020109
MacQueen, J.: Classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Oakland, CA, USA, pp. 281–297 (1967)
Malinen, M.I., Fränti, P.: BalancedK-means for clustering. In: Fränti, P., Brown, G., Loog, M., Escolano, F., Pelillo, M. (eds.) S+SSPR 2014. LNCS, vol. 8621, pp. 32–41. Springer, Heidelberg (2014).https://doi.org/10.1007/978-3-662-44415-3_4
Pianpak, P., Son, T.C.: DMAPF: A decentralized and distributed solver for multi-agent path finding problem with obstacles. Electron. Proc. Theor. Comput. Sci. (EPTCS)345, 99–112 (2021).https://doi.org/10.4204/eptcs.345.24. Sep
Pianpak, P., Son, T.C., Toups, Z.O., Yeoh, W.: A distributed solver for multi-agent path finding problems. In: Proceedings of the First International Conference on Distributed Artificial Intelligence (DAI), pp. 1–7 (2019).https://doi.org/10.1145/3356464.3357702
Salzman, O., Stern, R.Z.: Research challenges and opportunities in multi-agent path finding and multi-agent pickup and delivery problems blue sky ideas track. In: 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020, pp. 1711–1715. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) (2020).http://www.orensalzman.com/docs/AAMAS20.pdf
Silver, D.: Cooperative pathfinding. In: Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 117–122 (2005).https://ojs.aaai.org/index.php/AIIDE/article/view/18726
Stern, R.: Multi-agent path finding - an overview. In: Osipov, G.S., Panov, A.I., Yakovlev, K.S. (eds.) Artificial Intelligence. LNCS (LNAI), vol. 11866, pp. 96–115. Springer, Cham (2019).https://doi.org/10.1007/978-3-030-33274-7_6
Stern, R., et al.: Multi-agent pathfinding: Definitions, variants, and benchmarks. Symposium on Combinatorial Search (SoCS), pp. 151–158 (2019).https://www.aaai.org/ocs/index.php/SOCS/SOCS19/paper/view/18341
Wilt, C.M., Botea, A.: Spatially distributed multiagent path planning. In: Twenty-Fourth International Conference on Automated Planning and Scheduling, pp. 332–340 (2014).https://www.aaai.org/ocs/index.php/ICAPS/ICAPS14/paper/view/7858/8043
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New Mexico State University, Las Cruces, NM, USA
Poom Pianpak & Tran Cao Son
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Özyeğin University, Istanbul, Turkey
Reyhan Aydoğan
Universitat Politècnica de València, Valencia, Spain
Natalia Criado
Université Paris-Dauphine, Paris, France
Jérôme Lang
Universitat Politècnica de València, Valencia, Spain
Victor Sanchez-Anguix
King's College London, London, UK
Marc Serramia
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Pianpak, P., Son, T.C. (2023). Improving Problem Decomposition and Regulation in Distributed Multi-Agent Path Finder (DMAPF). In: Aydoğan, R., Criado, N., Lang, J., Sanchez-Anguix, V., Serramia, M. (eds) PRIMA 2022: Principles and Practice of Multi-Agent Systems. PRIMA 2022. Lecture Notes in Computer Science(), vol 13753. Springer, Cham. https://doi.org/10.1007/978-3-031-21203-1_10
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