Virtual worlds inhabited by synthetic agents are increasingly being used for training and education (e.g., [12,7,16]). These virtual worlds can provide a highly engaging environment in which to develop skills that students can readily apply to real world tasks. In creating these environments, considerable effort is often expended so that the simulated world more faithfully mirrors the real world in which the acquired skill must be employed. This concern for the fidelity of the training simulation has a long standing tradition (e.g., [4]).
Training teams of people to operate in dynamic multi-agent settings presents special challenges, however. The concern for fidelity has led to training environments that are in turn very dynamic. To faithfully capture the unpredictable multi-agent setting, simulations can be inhabited by multitudes of synthetic agents which ideally exhibit the same kind of complex behaviors that human participants would exhibit. For example, Distributed Interactive Simulation (DIS) training sessions involve teams of human students interacting with potentially thousands of synthetic and human agents within a very dynamic battlefield simulation (e.g., [16]).
From an instructor's perspective, the use of very dynamic multi-agent virtual environments raises several concerns. Among these is the seemingly simple question of what the student teams are currently doing. In a complex simulated world, conditions in the simulation can be difficult to determine. For instance, information from any one student's perception of events may be unavailable, or an agent (human or synthetic) may not be able to explain their own motives. Furthermore, abstracting from the behavior of individuals to the behavior and goals of teams requires additional effort. Conversely, there may be too much low level information for the instructor to absorb and interpret. And as the number of interacting agents grows, the difficulty increases.
Accordingly, it may be difficult to determine what teams are doing and why they are doing it. Furthermore, determining how well they are doing and analyzing trends in their behavior are also difficult. This is especially true in a world where interaction is not tightly scripted. Students may undergo unique experiences for which there is no set response against which they can be assessed.
We address the instructor's problem with a synthetic pedagogical agent, the PuppetMaster, which provides a high-level interpretation and assessment of the students. As the teacher's intermediary, the PuppetMaster dynamically assigns intelligent probes that monitor student teams and synthetic agents in the simulation. The situation in the virtual world is then assessed from the perspective of high level training objectives. The assessment includes events, trends and aggregate reporting over multiple entities (e.g., for revealing teamwork). The resulting evaluation is then used to compose 2-D and 3-D presentations that reduce the instructor's effort during the exercise and assists the instructor's review with students after the exercise.
In a very dynamic simulation, student teams are often learning to operate in both goal-directed and reactive fashions. Therefore, it is particularly critical that the PuppetMaster appropriately model the students' loosely scripted interactions with the world, in the face of changing goals, partial information and irrelevant information. This presents a problem for approaches to pedagogical agents and intelligent tutoring that attempt to model students based on detailed plan matching (e.g., [12]), model tracing (e.g., [1]) or plan recognition (e.g., [5]) based on a step-by-step analysis of actions. Arguably, such approaches, even if plausible for the multi-agent dynamic domain we are investigating, are often irrelevant for the instructional support that is needed.
To model student teams, we have adapted a model from reactive planning research, the Situation Space [14,9]. Roughly, a situation space is a structuring of states of the world into classes of problem solving histories whereby an agent's recognition of its current situation guides its goal-directed behavior. The PuppetMaster models both reactive and goal driven behaviors within the framework of a situation space model of the student team. The current situation provides a top-down focus for monitoring, inferring missing information and assessing behavioral trends, based on appropriateness for the students' situation.