Distributed constraint satisfaction: foundations of cooperation in multi-agent systems
Distributed constraint satisfaction: foundations of cooperation in multi-agent systems
Distributed Algorithms
CSPLIB: A Benchmark Library for Constraints
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
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
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Asynchronous Forward-Bounding for Distributed Constraints Optimization
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Quality guarantees on k-optimal solutions for distributed constraint optimization problems
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
Local search techniques for large high school timetabling problems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Adaptive price update in distributed Lagrangian relaxation protocol
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Directed soft arc consistency in pseudo trees
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Distributed Constraint Optimization for Large Teams of Mobile Sensing Agents
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
When should there be a "Me" in "Team"?: distributed multi-agent optimization under uncertainty
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Local search for distributed asymmetric optimization
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
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
Pseudo-tree-based incomplete algorithm for distributed constraint optimization with quality bounds
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
Distributed Gibbs: a memory-bounded sampling-based DCOP algorithm
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Hi-index | 0.00 |
Most former studies of Distributed Constraint Optimization Problems (DisCOPs) search considered only complete search algorithms, which are practical only for relatively small problems. Distributed local search algorithms can be used for solving DisCOPs. However, because of the differences between the global evaluation of a system's state and the private evaluation of states by agents, agents are unaware of the global best state which is explored by the algorithm. Previous attempts to use local search algorithms for solving DisCOPs reported the state held by the system at the termination of the algorithm, which was not necessarily the best state explored. A general framework for implementing distributed local search algorithms for DisCOPs is proposed. The proposed framework makes use of a BFS-tree in order to accumulate the costs of the system's state in its different steps and to propagate the detection of a new best step when it is found. The resulting framework enhances local search algorithms for DisCOPs with the anytime property. The proposed framework does not require additional network load. Agents are required to hold a small (linear) additional space (beside the requirements of the algorithm in use). The proposed framework preserves privacy at a higher level than complete Dis-COP algorithms which make use ofa pseudo-tree (ADOPT, DPOP).