Numerical recipes: the art of scientific computing
Numerical recipes: the art of scientific computing
Introduction to Distributed Algorithms
Introduction to Distributed Algorithms
Collectives and Design Complex Systems
Collectives and Design Complex Systems
Selfish Routing and the Price of Anarchy
Selfish Routing and the Price of Anarchy
An integrated token-based algorithm for scalable coordination
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Stochastic Approximations and Differential Inclusions
SIAM Journal on Control and Optimization
Correctness of Local Probability Propagation in Graphical Models with Loops
Neural Computation
A potential game approach to distributed power control and scheduling
Computer Networks: The International Journal of Computer and Telecommunications Networking
Stochastic Approximations and Differential Inclusions, Part II: Applications
Mathematics of Operations Research
Principles of Constraint Programming
Principles of Constraint Programming
From External to Internal Regret
The Journal of Machine Learning Research
An Application of Automated Negotiation to Distributed Task Allocation
IAT '07 Proceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Computing correlated equilibria in multi-player games
Journal of the ACM (JACM)
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
Decentralised coordination of low-power embedded devices using the max-sum algorithm
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
A multi-agent simulation system for prediction and scheduling of aero engine overhaul
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: industrial track
Agent Technologies for Sensor Networks
IEEE Intelligent Systems
Decentralized control of adaptive sampling in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
Decentralised dynamic task allocation: a practical game: theoretic approach
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Journal of Artificial Intelligence Research
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Quality guarantees on k-optimal solutions for distributed constraint optimization problems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint 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
Artificial Intelligence - Special issue: Distributed constraint satisfaction
The distributed breakout algorithms
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Decentralised coordination of mobile sensors using the max-sum algorithm
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Cooperative control and potential games
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The breakout method for escaping from local minima
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
The generalized distributive law
IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Bounded approximate decentralised coordination via the max-sum algorithm
Artificial Intelligence
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Multirobot coordination in pick-and-place tasks on a moving conveyor
Robotics and Computer-Integrated Manufacturing
Local coordination in online distributed constraint optimization problems
EUMAS'11 Proceedings of the 9th European conference on Multi-Agent Systems
Dynamic multiagent load balancing using distributed constraint optimization techniques
Web Intelligence and Agent Systems
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Distributed constraint optimization problems (DCOPs) are important in many areas of computer science and optimization. In a DCOP, each variable is controlled by one of many autonomous agents, who together have the joint goal of maximizing a global objective function. A wide variety of techniques have been explored to solve such problems, and here we focus on one of the main families, namely iterative approximate best-response algorithms used as local search algorithms for DCOPs. We define these algorithms as those in which, at each iteration, agents communicate only the states of the variables under their control to their neighbours on the constraint graph, and that reason about their next state based on the messages received from their neighbours. These algorithms include the distributed stochastic algorithm and stochastic coordination algorithms, the maximum-gain messaging algorithms, the families of fictitious play and adaptive play algorithms, and algorithms that use regret-based heuristics. This family of algorithms is commonly employed in real-world systems, as they can be used in domains where communication is difficult or costly, where it is appropriate to trade timeliness off against optimality, or where hardware limitations render complete or more computationally intensive algorithms unusable. However, until now, no overarching framework has existed for analyzing this broad family of algorithms, resulting in similar and overlapping work being published independently in several different literatures. The main contribution of this paper, then, is the development of a unified analytical framework for studying such algorithms. This framework is built on our insight that when formulated as non-cooperative games, DCOPs form a subset of the class of potential games. This result allows us to prove convergence properties of iterative approximate best-response algorithms developed in the computer science literature using game-theoretic methods (which also shows that such algorithms can also be applied to the more general problem of finding Nash equilibria in potential games), and, conversely, also allows us to show that many game-theoretic algorithms can be used to solve DCOPs. By so doing, our framework can assist system designers by making the pros and cons of, and the synergies between, the various iterative approximate best-response DCOP algorithm components clear.