  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.
