This paper studies a team coordination problem in a graph environment. Specifically, we incorporate "support" action which an agent can take to reduce the cost for its teammate to traverse some edges that have higher costs otherwise. Due to this added feature, the graph traversal is no longer a standard multi-agent path planning problem. To solve this new problem, we propose a novel formulation by posing it as a planning problem in the joint state space: the joint state graph (JSG). Since the edges of JSG implicitly incorporate the support actions taken by the agents, we are able to now optimize the joint actions by solving a standard single-agent path planning problem in JSG. One main drawback of this approach is the curse of dimensionality in both the number of agents and the size of the graph. To improve scalability in graph size, we further propose a hierarchical decomposition method to perform path planning in two levels. We provide complexity analysis as well as a statistical analysis to demonstrate the efficiency of our algorithm.

翻译：本文研究了图环境中的团队协作问题。具体而言，我们引入了“支援”动作，智能体可通过此动作降低其队友在原本成本较高的边上移动所需代价。由于这一新增特性，图遍历不再是标准的多智能体路径规划问题。为解决这一新问题，我们提出了一种新颖的建模方法，将其转化为联合状态空间中的规划问题：联合状态图（JSG）。由于JSG的边隐式地包含了智能体采取的支援动作，我们得以通过求解JSG中的标准单智能体路径规划问题来优化联合动作。该方法的主要缺点在于智能体数量和图规模均会引发维度灾难。为提升对图规模的扩展性，我们进一步提出了一种层次化分解方法，可在两个层级上执行路径规划。我们提供了复杂度分析及统计分析，以证明算法的效率。