Over the past two decades, the use of agent-based technology within simulated training environments has increased. Intelligent Tutoring Systems (ITS) technology may include reactive or proactive simulation agents that monitor and support computer-based training without human tutors. Reactive agents are able to provide hints and feedback on trainee performance within static scenarios. Based on the trainee's competency and their progress toward training objectives, proactive ITS use computational methods in real-time to decide when to change content, complexity and/or instructional methods within a training scenario (Niehaus & Riedl, 2009). This paper evaluates the advantages and disadvantages of reactive and proactive agents in computer-based tutoring systems; and discusses design considerations for the use of reactive and proactive agents in training simulations.

Historically, intelligent tutoring agents have been simple, passive observers within simulation environments. These reactive agents monitor the trainee's progress and provide hints or other feedback only when there is sufficient variance from expected norms. Reactive agent actions are often based on simple heuristics or scripted behaviors. This can be desirable if the goal of the training is repeatability. However, reactive agents often know little about the trainee and the training context beyond performance data.

Proactive agents have a higher computational cost in that they need to sense and understand more about the trainee, environment and training context, but are better able to predict trainee needs and adapt both feedback and scenario content. Complex military scenarios (e.g. ill-defined domains like bilateral negotiations) provide the opportunity to use more proactive agent techniques in assessing individual and team performance, and in adapting training scenarios to maintain challenge and flow.