Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
Algorithms for Distributed Constraint Satisfaction: A Review
Autonomous Agents and Multi-Agent Systems
The Evolution of Customer Middleware Requirements
PDIS '94 Proceedings of the Third International Conference on Parallel and Distributed Information Systems
Communication and Computation in Distributed CSP Algorithms
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Distributed breakout revisited
Eighteenth national conference on Artificial intelligence
A planning/scheduling methodology for the constrained resource problem
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Look-ahead value ordering for constraint satisfaction problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Applying interchangeability techniques to the distributed breakout algorithm
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
The breakout method for escaping from local minima
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Optimization-based dynamic sensor management for distributed multitarget tracking
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Asynchronous inter-level forward-checking for DisCSPs
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Hi-index | 0.00 |
In this paper we develop a localized value-ordering heuristic for distributed resource allocation problems. We show how this value ordering heuristics can be used to achieve desirable properties (increased effectiveness, or better allocations). The specific distributed resource allocation problem that we consider is sensor allocation in sensor networks, and the algorithmic skeleton that we use to experiment this heuristic is the distributed breakout algorithm. We compare this technique with the standard DBA and with another value-ordering heuristic [10] and see from the experimental results that it significantly outperforms both of them in terms of the number of cycles required to solve the problem (and therefore improvements in terms of communication and time requirements), especially when the problems are difficult. The resulting algorithm is also able to solve a higher percentage of the test problems. We show that a simple variation of this technique exhibits an interesting competition behavior that could be used to achieve higher quality allocations of the resource pool. Moreover, combinations of the two methods are possible, leading to interesting results. Finally, we note that this heuristic is domain, but not algorithm specific (meaning that it could most likely give good results in conjunction with other DisCSP algorithms as well). Content Areas: constraint satisfaction, distributed AI, problem solving