发表机构
Carnegie Mellon University; Jet Propulsion Laboratory, California Institute of Technology; Strategy Robot, Inc.; Strategic Machine, Inc.; Optimized Markets, Inc.（卡内基梅隆大学; 喷气推进实验室，加州理工学院; 战略机器人公司; 战略机器公司; 优化市场公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究分布式约束优化问题，通过结合在线学习方法与迭代定价框架，将其应用于大规模卫星调度，在实际问题实例上取得近最优性能，满足观测请求比例远超最先进基线。
AI 中文摘要
分布式约束优化问题（DCOPs）为有限通信下的分布式决策提供了常用框架，但许多实际实例规模太大难以整体求解。我们从两个互补方向应对这一挑战。一方面重新审视DCOPs与潜在博弈的联系，将现代在线学习算法用于DCOPs找均衡，证明其与代表性不完全DCOP算法有竞争力。另一方面受大规模分散卫星调度启发，提出新框架将DCOP分为任务分配的高层元DCOP和调度的独立局部优化问题，开发迭代定价方法耦合两级。结合在线学习方法与迭代定价框架，在实际分散卫星调度问题实例上取得近最优性能，满足观测请求超99%，而最先进基线为87%。
英文摘要
Distributed constraint optimization problems (DCOPs) provide a popular framework for distributed decision making under limited communication, but many real-world instances are too large to solve monolithically. We address this challenge from two complementary directions. We revisit the connection between DCOPs and potential games, and adapt modern online learning algorithms for equilibrium finding to DCOPs. We show that these algorithms are competitive with representative incomplete DCOP algorithms. We then turn to decomposition frameworks for large-scale DCOPs, motivated by large-scale decentralized satellite scheduling. We propose a new framework that separates a DCOP into two interacting subproblems: a high-level meta-DCOP for task allocation, and independent local optimization problems for scheduling. To couple the two levels, we develop a novel iterative pricing method that updates the meta-level utilities using feedback from the local optimizers. Combining our online learning methods with our iterative pricing framework, we obtain near-optimal performance on real-world decentralized satellite scheduling problem instances, fulfilling over 99% of observation requests compared with 87% for state-of-the-art baselines.
