Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
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
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Multiply-constrained distributed constraint optimization
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
ODPOP: an algorithm for open/distributed constraint optimization
AAAI'06 Proceedings of the 21st 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
Sensitivity analysis for distributed optimization with resource constraints
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Distributed constraint optimization with structured resource constraints
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Adaptive price update in distributed Lagrangian relaxation protocol
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Reducing the search space of resource constrained DCOPs
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
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
| Hi-index | 0.00 |
Cooperative problem solving with resource constraints are important in practical multi-agent systems. Resource constraints are necessary to handle practical problems including distributed task scheduling with limited resource availability. A dedicated framework called Resource Constrained DCOP (RCDCOP) has recently been proposed. RCDCOP models objective functions and resource constraints separately. A resource constraint is an n-ary constraint that represents the limit on the number of resources of a given type available to agents. Previous research addressing RCDCOPs employs the Adopt algorithm, which is an efficient solver for DCOPs. An important graph structure for Adopt is the pseudo-tree for constraint networks. A pseudo-tree implies a partial ordering of variables. In this variable ordering, n-ary constrained variables are placed on a single path of the tree. Therefore, resource constraints that have large arity augment the depth of the pseudo-tree. This also reduces the parallelism, and therefore the efficiency of Adopt. In this paper we propose another version of the Adopt algorithm for RCDCOP using a pseudo-tree that is generated ignoring resource constraints. The proposed method reduces the previous limitations in the construction of RCDCOP pseudo-trees. The key ideas of our work are as follows: (i) The pseudo-tree is generated ignoring resource constraints. (ii) Virtual variables are introduced, representing the usage of resources. These virtual variables are used to share resources among sub-trees. However, the addition of virtual variables increases the search space. To handle this problem, influence of placement of virtual variables/resources constraints in the pseudo tree is considered. Moreover the search is pruned using the bounds defined by the resource constraints if possible. These ideas are used to extend Adopt. The efficiency of our technique depends on the class of problems being considered, and we describe the obtained experimental results.