Underlying relationships among multi-agent systems (MAS) in hazardous scenarios can be represented as Game-theoretic models. We measure the performance of MAS achieving tasks from the perspective of balancing success probability and system costs. This paper proposes a new network-based model called Game-theoretic Utility Tree (GUT), which decomposes high-level strategies into executable low-level actions for cooperative MAS decisions. This is combined with a new payoff measure based on agent needs for real-time strategy games. We present an Explore game domain to evaluate GUT against the state-of-the-art QMIX decision-making method. 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 a higher winning rate.

翻译：危险情景下多试剂系统(MAS)之间基础关系可以作为游戏理论模型来表示。我们从平衡成功概率和系统成本的角度来衡量MAS完成任务的情况。本文提出了一个新的网络模型,称为游戏理论实用树(GUT),该模型将高层次战略分解成可执行的低层次合作MAS决定的行动。这与基于实时战略游戏代理需求的新报酬措施相结合。我们提出了一个探索游戏域,用最先进的QMIX决策方法来评价GUT。广泛的数字模拟结果显示,GUT可以在MAS合作中组织更复杂的关系,帮助小组以较低的成本和更高的赢率完成具有挑战性的任务。