Abstract
Many agent coordination problems can be modeled as distributed constraint optimization (DCOP) problems. ADOPT is an asynchronous and distributed search algorithm that is able to solve DCOP problems optimally. In this paper, we introduce Iterative Decreasing Bound ADOPT (IDB-ADOPT), a modification of ADOPT that changes the search strategy of ADOPT from performing one best-first search to performing a series of depth-first searches. Each depth-first search is provided with a bound, initially a large integer, and returns the first solution whose cost is smaller than or equal to the bound. The bound is then reduced to the cost of this solution minus one and the process repeats. If there is no solution whose cost is smaller than or equal to the bound, it returns a cost-minimal solution. Thus, IDB-ADOPT is an anytime algorithm that solves DCOP problems with integer costs optimally. Our experimental results for graph coloring problems show that IDB-ADOPT runs faster (that is, needs fewer cycles) than ADOPT on large DCOP problems, with savings of up to one order of magnitude.
This research was done while Ariel Felner spent his sabbatical at the University of Southern California, visiting Sven Koenig. This research has been partly supported by an NSF award to Sven Koenig under contract IIS-0350584. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied of the sponsoring organizations, agencies, companies or the U.S. government.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ali, S., Koenig, S., Tambe, M.: Preprocessing techniques for accelerating the DCOP algorithm ADOPT. In: Proceedings of AAMAS, pp. 1041–1048 (2005)
Bowring, E., Tambe, M., Yokoo, M.: Multiply-constrained distributed constraint optimization. In: Proceedings of AAMAS, pp. 1413–1420 (2006)
Chechetka, A., Sycara, K.: No-commitment branch and bound search for distributed constraint optimization. In: Proceedings of AAMAS, pp. 1427–1429 (2006)
Davin, J., Modi, J.: Hierarchical variable ordering for multiagent agreement problems. In: Proceedings of AAMAS, pp. 1433–1435 (2006)
Dechter, R., Pearl, J.: Generalized best-first search strategies and the optimality of A*. Journal of the Association for Computing Machinery 32(3), 505–536 (1985)
Korf, R.: Linear-space best-first search. Artificial Intelligence 62(1), 41–78 (1993)
Lesser, V., Ortiz, C., Tambe, M. (eds.): Distributed Sensor Networks: A Multiagent Perspective. Kluwer, Dordrecht (2003)
Maheswaran, R., Tambe, M., Bowring, E., Pearce, J., Varakantham, P.: Taking DCOP to the real world: Efficient complete solutions for distributed event scheduling. In: Proceedings of AAMAS, pp. 310–317 (2004)
Mailler, R., Lesser, V.: Solving distributed constraint optimization problems using cooperative mediation. In: Proceedings of AAMAS, pp. 438–445 (2004)
Modi, P., Shen, W., Tambe, M., Yokoo, M.: ADOPT: Asynchronous distributed constraint optimization with quality guarantees. Artificial Intelligence 161(1-2), 149–180 (2005)
Pecora, F., Modi, J., Scerri, P.: Reasoning about and dynamically posting n-ary constraints in ADOPT. In: Proceedings of the Distributed Constraint Reasoning Workshop (2006)
Petcu, A., Faltings, B.: A scalable method for multiagent constraint optimization. In: Proceedings of IJCAI, pp. 1413–1420 (2005)
Scerri, P., Modi, P., Tambe, M., Shen, W.: Are multiagent algorithms relevant for real hardware? A case study of distributed constraint algorithms. In: Proceedings of the ACM Symposium on Applied Computing, pp. 38–44 (2003)
Schurr, N., Okamoto, S., Maheswaran, R., Scerri, P., Tambe, M.: Evolution of a teamwork model. In: Sun, R. (ed.) Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation, pp. 307–327. Cambridge University Press, Cambridge (2005)
Zhang, W., Korf, R.: Performance of linear-space search algorithms. Artificial Intelligence 79(2), 241–292 (1995)
Zilberstein, S.: Operational Rationality through Compilation of Anytime Algorithms. PhD thesis, Computer Science Department, University of California, Berkeley (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yeoh, W., Felner, A., Koenig, S. (2009). IDB-ADOPT: A Depth-First Search DCOP Algorithm. In: Oddi, A., Fages, F., Rossi, F. (eds) Recent Advances in Constraints. CSCLP 2008. Lecture Notes in Computer Science(), vol 5655. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03251-6_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-03251-6_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03250-9
Online ISBN: 978-3-642-03251-6
eBook Packages: Computer ScienceComputer Science (R0)