This work is closely related to intelligent tutoring systems that employ some form of student modeling (e.g., [1,12,17,3]) Student modeling typically assumes some task that needs to be performed and some overall plan(s) of action or method(s) are known to achieve that task. In turn, the plan is used to interpret student actions so as to determine, for example, when a student is making a mistake or is at an impasse. Model tracing [1] differs somewhat from this approach in that a production system is used as a cognitive model of the method and the events generated by its execution behavior are matched to the student's behavior. Such approaches are tied to detailed modelling and matching of the actions or events that will occur. Therefore, they tend to have ready application when the skill to be acquired can be relatively tightly scripted both in terms of what specific actions need to be taken and the order in which they should be taken. Further, they presume a complete model of the problem solving task in the form of a plan or a production system which can be used to draw inferences about student behavior.
The pedagogical goals for the domains we have been considering, however, represent the other extreme. On the battlefield, there are multiple tasks to be performed and multiple agents/teams performing those tasks. Moreover, there is no overall plan of action either at the individual or team level that can be guaranteed to achieve the goal; events can enfold in such a way that obviates any plan. Rather student behavior is tied to the situation the student teams are in. The situation strongly impacts what goals need to be achieved and what tasks are to be performed. This view gains support from the fact that it is consistent with how the Army lays out its exercise plan as well as how it evaluates the student teams.
There are student modeling techniques that lie between these extremes (e.g., [3,12]). For instance, Situated Plan Attribution [3] is a system for tutoring satellite link operators. Because of the interactive, dynamic nature of the operator's task, Situated Plan Attribution attempts to loosen how actions and their goals are fit into an overall plan. Nevertheless the intent to do a detailed fitting still exists and the plan structure, a temporal dependency network, poses stricter causal relations than the Situation Space places on the transitions between situation. The differences between the two techniques follow rather directly from differences in the tutoring domains they focus on and the kinds of tutoring interaction that is required in those domains.
Our work is somewhat more loosely related to plan recognition (e.g., [13,5] and agent tracking (e.g., [15]). Plan recognition involves inferring an agent's unknown plan based on their actions. Our pedagogical agent work differs in several ways. The intent is not to infer a plan. We have knowledge of the abstract plan. Rather the issue is evaluating how well the various student behaviors serve the objectives of the plan. Furthermore, plan recognition often presumes plans that are step-by-step procedures. This is inconsistent with the nature of the reactive plans we are considering - very abstract goal decompositions represented only indirectly by alternative paths through the situation space. Our concern with reactive components in behavior is shared by the agent tracking work (e.g., [15]) which does not assume plans are step-by-step procedure but rather a mix of plan-based and reactive procedures. In contrast to the intent of our work, agent tracking does share with plan recognition the goal of inferring an agent's plan. As a consequence, it assumes access to the information and agent's action models that can support that analysis. Finally, in such action-based approaches, there would need to be a way to fit the modeling of an individual agent's behavior into the modeling of team behavior, which is our focus.