The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
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
Nogood based asynchronous distributed optimization (ADOPT ng)
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Multiply-constrained distributed constraint optimization
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Agent-Based Manufacturing and Control Systems: New Agile Manufacturing Solutions for Achieving Peak Performance (APICS Series on Resource Management)
A methodology for dynamic enterprise process performance evaluation
Computers in Industry
A Hybrid of Inference and Local Search for Distributed Combinatorial Optimization
IAT '07 Proceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology
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
PC-DPOP: a new partial centralization algorithm for distributed optimization
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
MB-DPOP: a new memory-bounded algorithm for distributed optimization
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A scalable method for multiagent constraint optimization
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A Class of Algorithms for Distributed Constraint Optimization: Volume 194 Frontiers in Artificial Intelligence and Applications
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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This paper introduces MULBS, a new DCOP (distributed constraint optimization problem) algorithm and also presents a DCOP formulation for scheduling of distributed meetings in collaborative environments. Scheduling in CSCWD can be seen as a DCOP where variables represent time slots and values are resources of a production system (machines, raw-materials, hardware components, etc.) or management system (meetings, project tasks, human resources, money, etc). Therefore, a DCOP algorithm must find a set of variable assignments that maximize an objective function taking constraints into account. However, it is well known that such problems are NP-complete and that more research must be done to obtain feasible and reliable computational approaches. Thus, DCOP emerges as a very promising technique: the search space is decomposed into smaller spaces and agents solve local problems, collaborating in order to achieve a global solution. We show with empirical experiments that MULBS outperforms some of the state-of-the-art algorithms for DCOP, guaranteeing high quality solutions using less computational resources for the distributed meeting scheduling task.