Abstract
Modeling an intelligent adversary has provided great challenges to simulated training realism. Traditional approaches to modeling have relied on rule-based and analytical decision-making models in an attempt to optimize the decision making of an intelligent computer-generated adversary. In order to promote realistic transfer of training in the realm of command and control, the trainee must experience realistic decision-making behavior in the enemy. This means that the enemy must make realistic decisions based on environmental constraints, goals, and intent. The enemy's decisions must then be reflected by the simulated agents. The Recognition-Primed Decision (RPD) Model is a descriptive model of expert decision making in real-world settings. We have several related projects where the goal is to translate the conceptual RPD Model into a computer model that can simulate realistic expert decision making. In attempting this feat, we have discovered many valuable lessons about modeling cognition and decision making, and about the assumptions and mechanisms underlying the RPD Model. The purpose of this paper is to report those findings.
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