Integration of Independence Detection into SAT-based Optimal Multi-Agent Path Finding - A Novel SAT-based Optimal MAPF Solver
Pavel Surynek, Jiří Švancara, Ariel Felner, Eli Boyarski
2017
Abstract
The problem of optimal multi-agent path finding (MAPF) is addressed in this paper. The task is to find optimal paths for mobile agents where each of them need to reach a unique goal position from the given start with respect to the given cost function. Agents must not collide with each other which is a source of combinatorial difficulty of the problem. An abstraction of the problem where discrete agents move in an undirected graph is usually adopted in the literature. Specifically, it is shown in this paper how to integrate independence detection (ID) technique developed for search based MAPF solving into a compilation-based technique that translates the instance of the MAPF problem into propositional satisfiability formalism (SAT). The independence detection technique allows decomposition of the instance consisting of a given number of agents into instances consisting of small groups of agents with no interaction across groups. These small instances can be solved independently and the solution of the original instance is combined from small solutions eventually. The reduction of the size of instances translated to the target SAT formalism has a significant impact on performance as shown in the presented experimental evaluation. The new solver integrating SAT translation and the independence detection is shown to be state-of-the-art in its class for optimal MAPF solving.
References
- Audemard, G., Simon, L., 2013. The Glucose SAT Solver. http://labri.fr/perso/lsimon/glucose/, 2013, [accessed in October 2016].
- Audemard, G., Simon, L., 2009. Predicting Learnt Clauses Quality in Modern SAT Solvers. Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI 2009), pp. 399-404, IJCAI.
- Balint, A., Belov, A., Heule, M., Järvisalo, M., 2015. SAT 2015 competition. http://www.satcompetition.org/, 2015, [accessed in October 2016].
- Berg, J. van den, Snoeyink, J., Lin, M. C., Manocha, D., 2010. Centralized path planning for multiple robots: Optimal decoupling into sequential plans. Proceedings of Robotics: Science and Systems V, University of Washington, 2009, The MIT Press.
- Biere, A., Heule, M., van Maaren, H., Walsh, T., 2009. Handbook of Satisfiability. IOS Press.
- Boyarski, E., Felner, A., Stern, R., Sharon, G., Tolpin, D., Betzalel, O., Shimony, S.: ICBS: Improved ConflictBased Search Algorithm for Multi-Agent Pathfinding. Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), pp. 740-746, IJCAI.
- Cáp, M., Novák, P., Vokrínek, J., Pechoucek, M., 2013. Multi-agent RRT: sampling-based coop-erative pathfinding. International conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2013), pp. 1263-1264, IFAAMAS.
- Erdem, E., Kisa, D. G., Öztok, U., Schüller, P., 2013. A General Formal Framework for Pathfinding Problems with Multiple Agents. Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI 2013), AAAI Press.
- Huang, R., Chen, Y., Zhang, W., 2010. A Novel Transition Based Encoding Scheme for Planning as Satisfiability. Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI 2010), AAAI Press.
- Kautz, H., Selman, B., 1999. Unifying SAT-based and Graph-based Planning. Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI 1999), pp. 318-325, Morgan Kaufmann.
- Kim, D., Hirayama, K., Park, G.-K, 2014. Collision Avoidance in Multiple-Ship Situations by Distributed Local Search. Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Volume 18(5), pp. 839-848, Fujipress.
- Kornhauser, D., Miller, G. L., Spirakis, P. G, 1984. Coordinating Pebble Motion on Graphs, the Diameter of Permutation Groups, and Applications. Proceedings of the 25th Annual Symposium on Foundations of Computer Science (FOCS 1984), pp. 241-250, IEEE Press.
- Ma, H., Koenig, S., Ayanian, N., Cohen, L., Hoenig W., Kumar, T.K.S., Uras, T., Xu. H., Tovey, C., Sharon, G., 2016. Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios. IJCAI-16 Workshop on Multi-Agent Path Finding (WOMPF).
- Michael, N., Fink, J., Kumar, V., 2011. Cooperative manipulation and transportation with aerial robots. Autonomous Robots, Volume 30(1), pp. 73-86, Springer.
- Ratner, D. and Warmuth, M. K., 1990. NxN Puzzle and Related Relocation Problems. Journal of Symbolic Computation, Volume 10 (2), pp. 111-138, Elsevier.
- Ryan, M. R. K., 2008. Exploiting Subgraph Structure in Multi-Robot Path Planning. Journal of Artificial Intelligence Research (JAIR), Volume 31, 2008, pp. 497- 542, AAAI Press.
- Ryan, M. R. K., 2010. Constraint-based multi-robot path planning. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2010), pp. 922-928, IEEE Press.
- Sharon, G., Stern, R., Goldenberg, M., Felner, A., 2013. The increasing cost tree search for optimal multi-agent pathfinding. Artificial Intelligence, Volume 195, pp. 470-495, Elsevier.
- Sharon, G., Stern, R., Felner, A., Sturtevant, N. R., 2015. Conflict-based search for optimal multi-agent pathfinding. Artificial Intelligence, 219, 40-66, Elsevier.
- Silver, D., 2005. Cooperative Pathfinding. Proceedings of the 1st Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE 2005), pp. 117-122, AAAI Press.
- Standley, T., 2010. Finding Optimal Solutions to Cooperative Pathfinding Problems. Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI2010), pp. 173-178, AAAI Press.
- Standley, T., Korf, R. E., 2011. Complete Algorithms for Cooperative Pathfinding Problems. Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI 2011), pp. 668-673, IJCAI.
- Sturtevant, N. R., 2012. Benchmarks for Grid-Based Pathfinding. IEEE Transactions on Computational Intelligence and AI in Games, Volume 4(2), pp. 144-148, IEEE Press.
- Surynek, P., 2009. A Novel Approach to Path Planning for Multiple Robots in Biconnected Graphs. Proceedings of the 2009 IEEE International Conference on Robotics and Automation (ICRA 2009), pp. 3613-3619, IEEE Press.
- Surynek, P., 2010. An Optimization Variant of Multi-Robot Path Planning is Intractable. Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI 2010), pp. 1261-1263, AAAI Press.
- Surynek, P., 2014. Compact Representations of Cooperative Path-Finding as SAT Based on Matchings in Bipartite Graphs. Proceedings of the 26th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2014), pp. 875-882, IEEE Computer Society.
- Surynek, P., Felner, A., Stern, R., Boyarski, E., 2016. Efficient SAT Approach to Multi-Agent Path Finding Under the Sum of Costs Objective. Proceedings of 22nd European Conference on Artificial Intelligence (ECAI 2016), pp. 810-818, IOS Press.
- Yu, J., LaValle, S. M., 2013a. Structure and intractability of optimal multirobot path planning on graphs. Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI 2013), AAAI Press.
- Yu, J., LaValle, S. M., 2013b. Planning optimal paths for multiple robots on graphs. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2013), pp. 3612-3617, IEEE Press.
- Wang, K. C., Botea, A., 2008. Fast and memory-efficient multi-agent pathfinding, Proceedings of the 18th International Conference on Automated Planning and Scheduling (ICAPS 2008), pp. 380-387, AAAI Press.
- de Wilde, B., ter Mors, A., Witteveen, C.: Push and Rotate: a Complete Multi-robot Pathfinding Algorithm. Journal of Artificial Intelligence Research (JAIR), Volume 51, pp. 443-492, AAAI Press, 2014.
- Wilson, R. M., 1974. Graph Puzzles, Homotopy, and the Alternating Group. Journal of Combinatorial Theory, Ser. B 16, pp. 86-96, Elsevier.
Paper Citation
in Harvard Style
Surynek P., Švancara J., Felner A. and Boyarski E. (2017). Integration of Independence Detection into SAT-based Optimal Multi-Agent Path Finding - A Novel SAT-based Optimal MAPF Solver . In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-220-2, pages 85-95. DOI: 10.5220/0006126000850095
in Bibtex Style
@conference{icaart17,
author={Pavel Surynek and Jiří Švancara and Ariel Felner and Eli Boyarski},
title={Integration of Independence Detection into SAT-based Optimal Multi-Agent Path Finding - A Novel SAT-based Optimal MAPF Solver},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2017},
pages={85-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006126000850095},
isbn={978-989-758-220-2},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Integration of Independence Detection into SAT-based Optimal Multi-Agent Path Finding - A Novel SAT-based Optimal MAPF Solver
SN - 978-989-758-220-2
AU - Surynek P.
AU - Švancara J.
AU - Felner A.
AU - Boyarski E.
PY - 2017
SP - 85
EP - 95
DO - 10.5220/0006126000850095