The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
Constraint Propagation for Soft Constraints: Generalization and Termination Conditions
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
Constraint Processing
Distributed Sensor Networks: A Multiagent Perspective
Distributed Sensor Networks: A Multiagent Perspective
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
Preprocessing techniques for accelerating the DCOP algorithm ADOPT
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
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
Experimental evaluation of preprocessing techniques in constraint satisfaction problems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Railroad Driving Model Based on Distributed Constraint Optimization
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
A scalable method for multiagent constraint optimization
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A sociologically inspired heuristic for optimization algorithms: A case study on ant systems
Expert Systems with Applications: An International Journal
A social approach for learning agents
Expert Systems with Applications: An International Journal
| Hi-index | 0.01 |
Distributed Constraint Optimization Problem (DCOP) has emerged as one of most important formalisms for distributed reasoning in multiagent systems. Nevertheless, there are few real world applications based on methods for solving DCOP, due to their inefficiency in some scenarios. This paper introduces the use of Social Network Analysis (SNA) techniques to improve the performance in pseudo-tree-based DCOP algorithms. We investigate when the SNA is useful and which techniques can be applied in some DCOP instances. To evaluate our proposal, we use the two most popular complete and optimal DCOP algorithms, named ADOPT and DPOP, and compare the obtained results with others well-known pre-processing techniques. The experimental results show that SNA techniques can speed up ADOPT and DPOP algorithms.