A sufficient condition for backtrack-bounded search
Journal of the ACM (JACM)
Arc consistency for soft constraints
Artificial Intelligence
Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Solving Distributed Constraint Optimization Problems Using Cooperative Mediation
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Preprocessing techniques for accelerating the DCOP algorithm ADOPT
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Evaluating the performance of DCOP algorithms in a real world, dynamic problem
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Decentralised coordination of low-power embedded devices using the max-sum algorithm
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
ODPOP: an algorithm for open/distributed constraint optimization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Anytime local search for distributed constraint optimization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
A scalable method for multiagent constraint optimization
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A quantified distributed constraint optimization problem
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm
Journal of Artificial Intelligence Research
Bounded approximate decentralised coordination via the max-sum algorithm
Artificial Intelligence
Reducing the search space of resource constrained DCOPs
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Considering Equality on Distributed Constraint Optimization Problem for Resource Supply Network
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
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We propose an efficient method that applies directed soft arc consistency to a Distributed Constraint Optimization Problem (DCOP) which is a fundamental framework of multi-agent systems. With DCOPs a multi-agent system is represented as a set of variables and a set of constraints/cost functions. We focus on DCOP solvers that employ pseudo-trees. A pseudo-tree is a graph structure for a constraint network that represents a partial ordering of variables. Most pseudo-tree-based search algorithms perform optimistic searches using explicit/implicit backtracking in parallel. However, for cost functions taking a wide range of cost values, such exact algorithms require many search iterations, even if the constraint density is relatively low. Therefore additional improvements are necessary to reduce the search process. A previous study used a dynamic programming-based preprocessing technique that estimates the lower bound values of costs. However, there are opportunities for further improvements of efficiency. In addition, modifications of the search algorithm are necessary to use the estimated lower bounds. The proposed method applies soft arc consistency (soft AC) enforcement to DCOP. In the proposed method, directed soft AC is performed based on a pseudo-tree in a bottom up manner. Using the directed soft AC, the global lower bound value of cost functions is passed up to the root node of the pseudo-tree. The value of each cost function is also reduced. As a result, the original problem is converted to an equivalent problem which is efficiently solved using common search algorithms. The performance of the proposed method is evaluated by experimentation. The results show that it is more efficient than previous methods that estimate the lower bound of costs. Moreover, the proposed method is efficient for approximation algorithms that use bounded errors.