Abstract
When solving Distributed Constraint Satisfaction Problems (DCSP), it is desirable that the search exploits asynchronism as much as possible so that the employed agents can perform much of the work in parallel. This allows to utilize the processing power available in a distributed environment. However, in many of todays DCSP algorithms, only a few agents are working at any given time and the others are idling. This is caused by the fact that once an agent is consistent with its neighbors, it becomes idling until it is forced by other agents to choose a different assignment for its local variables.
In this paper we propose a method that utilizes the idling time of the agents to increase the efficiency of a distributed backtracking algorithm where agents have complex local problems and share variables among them. An agent computes solutions to its local problem in advance while it is waiting for incoming messages. This means that when an agent finds a solution to the local problem that is consistent with higher order agents, it not only informs lower order agents but continuous to search for further solutions which then are stored in a queue. When the current local solution becomes invalid due to a nogood received from a lower order agent, the agent does not have to search for a new local solution but can retrieve a precomputed one from the queue. This approach increases the amount of work the agents can perform in parallel since higher order agents search ahead for local solutions while lower order agents are trying to expand the current partial solution.
Our experiments show that some increase in performance can be gained by queuing local solutions in distributed backtracking.
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
Yokoo, M., Durfee, E.H., Ishida, T., Kuwabara, K.: Distributed constraint satisfaction for formalizing distributed problem solving. In: International Conference on Distributed Computing Systems, pp. 614–621 (1992)
Yokoo, M., Durfee, E.H., Ishida, T., Kuwabara, K.: The distributed constraint satisfaction problem: Formalization and algorithms. Knowledge and Data Engineering 10, 673–685 (1998)
Bessière, C., Maestre, A., Meseguer, P.: Distributed dynamic backtracking. In: Proceedings of the IJCAI 2001 workshop on Distributed Constraint Reasoning, pp. 9–16 (2001)
Hamadi, Y., Bessière, C., Quinqueton, J.: Backtracking in distributed constraint networks. In: Proceedings ECAI 1998, pp. 219–223 (1998)
Armstrong, A., Durfee, E.H.: Dynamic prioritization of complex agents in distributed constraint satisfaction problems. In: AAAI 1997 Workshop on Constraints and Agents, pp. 8–13 (1997)
Yokoo, M.: Asynchronous weak-commitment search for solving distributed constraint satisfaction problems. In: Montanari, U., Rossi, F. (eds.) CP 1995. LNCS, vol. 976, pp. 88–102. Springer, Heidelberg (1995)
Silaghi, M.-C., Sam-Haroud, D., Faltings, B.: Asynchronous search with aggregations. In: AAAI/IAAI, pp. 917–922 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mueller, R., Havens, W.S. (2005). Queuing Local Solutions in Distributed Constraint Satisfaction Systems. In: Kégl, B., Lapalme, G. (eds) Advances in Artificial Intelligence. Canadian AI 2005. Lecture Notes in Computer Science(), vol 3501. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424918_12
Download citation
DOI: https://doi.org/10.1007/11424918_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25864-3
Online ISBN: 978-3-540-31952-8
eBook Packages: Computer ScienceComputer Science (R0)