Adopt: asynchronous distributed constraint optimization with quality guarantees

By P. J. Modi, Wei-Min Shen, Milind Tambe, Makoto Yokoo. Artificial Intelligence, 2005

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Applying max-sum to teams of mobile sensing agents (2018, neutral): “The DCOP literature offers a rich wealth of solution techniques, ranging from complete approaches (Modi et al., 2005), which are guaranteed to find the optimal solution, to heuristic methods (Zhang”
Decentralised coordination of low-power embedded devices using the max-sum algorithm (2008, methodology): “The graph colouring problem is a canonical DCOP problem [4, 8], and is one that has previously been used to benchmark coordination algorithms for sensor networks [1, 15].”
Exploiting Agent and Type Independence in Collaborative Graphical Bayesian Games (2011, methodology): “ssentially strips away the CGBG’s type independence, resulting in an exponential increase in the worst-case size of the payoff function. CGBGs can also be modeled as constraint optimization problems (Modi et al., 2005), for which some methods implicitly exploit type independence (Oliehoek et al., 20”
Bounded approximate decentralised coordination via the max-sum algorithm (2009, methodology): “Since both the bounded max-sum and ADOPT approaches are deterministic, the reported values are the utilities obtained on a single execution of each algorithm.”
Collaborative Multiagent Reinforcement Learning by Payoff Propagation (2006, methodology): “, 2005), sensor networks (Lesser et al., 2003; Modi et al., 2005), but also robotic teams, for example, exploration and mapping (Burgard et al.”