Underlying relationships among Multi-Agent Systems (MAS) in hazardous scenarios can be represented as Game-theoretic models. This paper proposes a new hierarchical network-based model called Game-theoretic Utility Tree (GUT), which decomposes high-level strategies into executable low-level actions for cooperative MAS decisions. It combines with a new payoff measure based on agent needs for real-time strategy games. We present an Explore game domain, where we measure the performance of MAS achieving tasks from the perspective of balancing the success probability and system costs. We evaluate the GUT approach against state-of-the-art methods that greedily rely on rewards of the composite actions. Conclusive results on extensive numerical simulations indicate that GUT can organize more complex relationships among MAS cooperation, helping the group achieve challenging tasks with lower costs and higher winning rates. Furthermore, we demonstrated the applicability of the GUT using the simulator-hardware testbed - Robotarium. The performances verified the effectiveness of the GUT in the real robot application and validated that the GUT could effectively organize MAS cooperation strategies, helping the group with fewer advantages achieve higher performance.

翻译：在危险场景下，多智能体系统(MAS)的内在关联可建模为博弈论模型。本文提出一种名为博弈论效用树(GUT)的新型层次化网络模型，该模型将高层策略分解为可执行的底层动作，用于协同MAS决策。该模型结合了基于智能体需求的新型收益度量方法，适用于实时策略游戏。我们构建了探索游戏域，从平衡成功概率与系统成本的角度衡量MAS任务完成性能。将GUT方法与当前贪婪依赖复合动作奖励的最先进方法进行对比评估。大量数值模拟的结论性结果表明，GUT能够组织更复杂的MAS协同关系，以更低成本与更高胜率帮助群体完成挑战性任务。此外，我们通过仿真器-硬件测试平台Robotarium验证了GUT的适用性。真实机器人应用中的性能表现验证了GUT的有效性，并证实该模型能有效组织MAS协同策略，帮助处于劣势的群体实现更优性能。