  Underlying relationships among multiagent systems (MAS) in hazardous
scenarios can be represented as game-theoretic models. In adversarial
environments, the adversaries can be intentional or unintentional based on
their needs and motivations. Agents will adopt suitable decision-making
strategies to maximize their current needs and minimize their expected costs.
This paper proposes and extends the new hierarchical network-based model,
termed Game-theoretic Utility Tree (GUT), to arrive at a cooperative pursuit
strategy to catch an evader in the Pursuit-Evasion game domain. We verify and
demonstrate the performance of the proposed method using the Robotarium
platform compared to the conventional constant bearing (CB) and pure pursuit
(PP) strategies. The experiments demonstrated the effectiveness of the GUT, and
the performances validated that the GUT could effectively organize cooperation
strategies, helping the group with fewer advantages achieve higher performance.
