Decentralized planning is a key element of cooperative multi-agent systems for information gathering tasks. However, despite the high frequency of agent failures in realistic large deployment scenarios, current approaches perform poorly in the presence of failures, by not converging at all, and/or by making very inefficient use of resources (e.g. energy). In this work, we propose Attritable MCTS (A-MCTS), a decentralized MCTS algorithm capable of timely and efficient adaptation to changes in the set of active agents. It is based on the use of a global reward function for the estimation of each agent's local contribution, and regret matching for coordination. We evaluate its effectiveness in realistic data-harvesting problems under different scenarios. We show both theoretically and experimentally that A-MCTS enables efficient adaptation even under high failure rates. Results suggest that, in the presence of frequent failures, our solution improves substantially over the best existing approaches in terms of global utility and scalability.

翻译：分散式规划是用于信息收集任务的协作多智能体系统的关键要素。然而，尽管在现实大规模部署场景中智能体故障频发，现有方法在存在故障时表现不佳，表现为完全不收敛和/或资源（如能量）利用效率极低。在本工作中，我们提出可损耗蒙特卡洛树搜索（A-MCTS），这是一种能够及时高效适应活跃智能体集合变化的分散式MCTS算法。其基础在于使用全局奖励函数来估计每个智能体的局部贡献，并通过遗憾匹配进行协调。我们在不同场景下的现实数据收集问题中评估其有效性。我们从理论和实验两方面证明，即使在高故障率下，A-MCTS仍能实现高效适应。结果表明，在频繁发生故障的情况下，我们的解决方案在全局效用和可扩展性方面较现有最佳方法有显著提升。