Distributed constraint optimization problems (DCOPs) are well-suited for modeling multi-agent coordination problems where the primary interactions are between local subsets of agents. However, one limitation of DCOPs is the assumption that the constraint rewards are without uncertainty. Researchers have thus extended DCOPs to Stochastic DCOPs (SDCOPs), where rewards are sampled from known probability distribution reward functions, and introduced algorithms to find solutions with the largest expected reward. Unfortunately, such a solution might be very risky, that is, very likely to result in a poor reward. Thus, in this pa per, we make three contributions: (1) we propose a stricter objective for SDCOPs, namely to find a solution with the most stochastically dominating probability distribution re ward function; (2) we introduce an algorithm to find such solutions; and (3) we show that stochastically dominating solutions can indeed be less risky than expected reward maximizing solutions.
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