  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.
