Coalition structure generation (CSG), i.e. the problem of optimally partitioning a set of agents into coalitions to maximize social welfare, is a fundamental computational problem in multiagent systems. This problem is important for many applications where small run times are necessary, including transportation and disaster response. In this paper, we develop SALDAE, a multiagent path finding algorithm for CSG that operates on a graph of coalition structures. Our algorithm utilizes a variety of heuristics and strategies to perform the search and guide it. It is an anytime algorithm that can handle large problems with hundreds and thousands of agents. We show empirically on nine standard value distributions, including disaster response and electric vehicle allocation benchmarks, that our algorithm enables a rapid finding of high-quality solutions and compares favorably with other state-of-the-art methods.

翻译：联盟结构生成（CSG），即通过最优划分智能体集合为若干联盟以最大化社会福利的问题，是多智能体系统中的基础计算问题。该问题对于许多需要短运行时间的应用至关重要，包括交通运输和灾难响应。本文提出SALDAE，一种在联盟结构图上运行的、用于CSG的多智能体路径搜索算法。本算法采用多种启发式策略执行搜索并引导搜索过程。它是一种随时可终止的算法，能够处理包含数百至数千智能体的大规模问题。我们在九种标准价值分布（包括灾难响应和电动汽车分配基准测试）上通过实验表明，本算法能够快速找到高质量解，并优于其他先进方法。