Encodings of non-binary constraint satisfaction problems
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
Secure Distributed Constraint Satisfaction: Reaching Agreement without Revealing Private Information
CP '02 Proceedings of the 8th 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
Distributed Constraint Satisfaction and Optimization with Privacy Enforcement
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Assigning Tasks in a 24-Hour Software Development Model
APSEC '04 Proceedings of the 11th Asia-Pacific Software Engineering Conference
A distributed framework for solving the Multiagent Plan Coordination Problem
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
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
A scalable method for multiagent constraint optimization
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
On k-optimal distributed constraint optimization algorithms: new bounds and algorithms
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Resource constrained distributed constraint optimization using resource constraint free pseudo-tree
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Distributed constraint optimization with structured resource constraints
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Resource constrained distributed constraint optimization with virtual variables
AAAI'08 Proceedings of the 23rd national 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
BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm
Journal of Artificial Intelligence Research
Balancing local resources and global goals in multiply-constrained DCOP
Multiagent and Grid Systems
Bounded decentralised coordination over multiple objectives
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Reducing the search space of resource constrained DCOPs
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
Distributed constraint optimization with MULBS: A case study on collaborative meeting scheduling
Journal of Network and Computer Applications
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Distributed constraint optimization (DCOP) has emerged as a useful technique for multiagent coordination. While previous DCOP work focuses on optimizing a single team objective, in many domains, agents must satisfy additional constraints on resources consumed locally (due to interactions within their local neighborhoods). Such resource constraints may be required to be private or shared for efficiency's sake. This paper provides a novel multiply-constrained DCOP algorithm for addressing these domains which is based on mutually-intervening search, i.e. using local resource constraints to intervene in the search for the optimal solution and vice versa. It is realized through three key ideas: (i) transforming n-ary constraints to maintain privacy; (ii) dynamically setting upper bounds on joint resource consumption with neighbors; and (iii) identifying if the local DCOP graph structure allows agents to compute exact resource bounds for additional efficiency. These ideas are implemented by modifying Adopt, one of the most efficient DCOP algorithms. Both detailed experimental results as well as proofs of correctness are presented.