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Toward Automatic Verification of Multiagent Systems for Training Simulations

Ning Wang, David V. Pynadath, Stacy C. Marsella

Advances in multiagent systems have led to their successful application in experiential training simulations, where students learn by interacting with agents who represent people, groups, structures, etc. These multiagent simulations must model the training scenario so that the students’ success is correlated with the degree to which they follow the intended pedagogy. As these simulations increase in size and richness, it becomes harder to guarantee that the agents accurately encode the pedagogy. Testing with human subjects provides the most accurate feedback, but it can explore only a limited subspace of simulation paths. In this paper, we present a mechanism for using human data to verify the degree to which the simulation encodes the intended pedagogy. Starting with an analysis of data from a deployed multiagent training simulation, we then present an automated mechanism for using the human data to generate a distribution appropriate for sampling simulation paths. By generalizing from a small set of human data, the automated approach can systematically explore a much larger space of possible training paths and verify the degree to which a multiagent training simulation adheres to its intended pedagogy.

The final publication is available at Springer via https://doi.org/10.1007/978-3-642-30950-2_20.