Bounded approximate decentralised coordination via the max-sum algorithm

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Deep Coordination Graphs (2019, background): “Coordination graphs (CG) have been extensively studied in multi-agent robotics with given payoffs (e.g. Rogers et al., 2011; Yedidsion et al., 2018).”
The Representational Capacity of Action-Value Networks for Multi-Agent Reinforcement Learning (2019, background): “a sum of smaller action-value functions Qe (ae ) defined over a coordination graph [7, 11, 21] describing these influences.”
Putting the 'smarts' into the smart grid (2012, background): “Hence, more scaleable decentralised planning approaches that rely on short range communication between individual actors (e.g., distribution network nodes, consumers, and EVs) will be needed [38] or [32].”
Applying max-sum to teams of mobile sensing agents (2018, methodology): “…the Max-sum algorithm, there is a significant body of work that focuses on different aspects of the algorithm such as, convergence guarantees (Rogers et al., 2011; Zivan and Peled, 2012), evaluation for realistic applications (Ramchurn et al., 2010) and computational complexity (Macarthur et…”
A Framework for Parallel and Distributed Training of Neural Networks (2016, background): “Some interesting results can derive by considering the literature on distributed constraint optimization problems (DCOP), which deals with distributed decision making problems where the decision variables are separated among the different agents (Modi et al., 2005; Rogers et al., 2011).”