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.
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