Abstract
Dynamic distributed constraint optimisation problems are a very effective tool for solving multi-agent problems. However they require protocols for agents to collaborate in optimising shared objectives in a decentralised manner without necessarily revealing all of their private constraints. In this paper, we present the details of the Support-Based Distributed Optimisation (SBDO) algorithm for solving dynamic distributed constraint optimisation problems. This algorithm is complete wrt hard constraints but not wrt objectives. Furthermore, we show that SBDO is completely asynchronous, sound and fault tolerant. Finally, we evaluate the performance of SDBO with respect to DynCOAA for DynDCOP and ADOPT, DPOP for DCOP. The results highlight that in general, SBDO out performs these algorithms on criteria such as time, solution quality, number of messages, non-concurrent constraint checks and memory usage.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Chechetka, A., Sycara, K.: No-commitment branch and bound search for distributed constraint optimization. In: AAMAS 2006, pp. 1427–1429. ACM (2006)
Harvey, P., Chang, C.F., Ghose, A.: Support-based distributed search: a new approach for multiagent constraint processing. In: AAMAS 2006, pp. 377–383. ACM (2006)
Kiekintveld, C., Yin, Z., Kumar, A., Tambe, M.: Asynchronous algorithms for approximate distributed constraint optimization with quality bounds. In: AAMAS, pp. 133–140 (2010)
Léauté, T., Ottens, B., Szymanek, R.: FRODO 2.0: An open-source framework for distributed constraint optimization. In: Proceedings of the IJCAI 2009 Distributed Constraint Reasoning Workshop (DCR 2009), Pasadena, California, USA, pp. 160–164 (July 13, 2009), http://liawww.epfl.ch/frodo/
Meisels, A., Kaplansky, E., Razgon, I., Zivan, R.: Comparing performance of distributed constraints processing algorithms. In: Proceedings of DCR Workshop, AAMAS 2002 (2002)
Mertens, K.: An Ant-Based Approach for Solving Dynamic Constraint Optimization Problems. PhD thesis, Katholieke Universiteit Leuven (December 2006)
Modi, P.J., Shen, W.-M., Tambe, M., Yokoo, M.: Adopt: asynchronous distributed constraint optimization with quality guarantees. Artificial Intelligence 161, 149–180 (2005)
Petcu, A., Faltings, B.: S-dpop: Superstabilizing, fault-containing multiagent combinatorial optimization. In: Proceedings of the National Conference on Artificial Intelligence, AAAI-2005, pp. 449–454. AAAI, Pittsburgh (July 2005)
Portway, C.P.: USC dcop repository (2008), http://teamcore.usc.edu/dcop
Schiex, T., Verfaillie, G.: Nogood recording for static and dynamic constraint satisfaction problems. In: TAI 1993, pp. 48–55 (1993)
Stranders, R., Farinelli, A., Rogers, A., Jennings, N.R.: Decentralised coordination of continuously valued control parameters using the max-sum algorithm. In: AAMAS, vol. (1), pp. 601–608 (2009)
Vinyals, M., Pujol, M., Rodríguez-Aguilar, J.A., Cerquides, J.: Divide-and-coordinate: Dcops by agreement. In: AAMAS, pp. 149–156 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Billiau, G., Chang, C.F., Ghose, A. (2012). SBDO: A New Robust Approach to Dynamic Distributed Constraint Optimisation. In: Desai, N., Liu, A., Winikoff, M. (eds) Principles and Practice of Multi-Agent Systems. PRIMA 2010. Lecture Notes in Computer Science(), vol 7057. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25920-3_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-25920-3_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25919-7
Online ISBN: 978-3-642-25920-3
eBook Packages: Computer ScienceComputer Science (R0)