Knowing the future states of an adversary in an adversary-avoidance game can impart a survival advantage. To assess how predictive modeling helps agents achieve goals and avoid adversaries, we tested the efficacy of three predictive algorithms within a gridworld-based game. For one predictive algorithm, model predictions of adversary moves furnished to an agent helped the agent avoid capture compared to a case without predictions. A human-machine team scenario also benefited from model predictions, while humans alone experienced a ceiling effect. We investigated the efficacy of two additional predictive algorithms and present a maritime vessel pursuit scenario.
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