Constructing Virtual Asymmetric Opponents from Data and Models in the Literature: Case of Crowd Rioting

This paper describes an effort to integrate human behavior models from a range of ability, stress, emotion, decision theoretic, and motivation literatures into a game-theoretic framework appropriate for representing synthetic asymmetric agents and scenarios. Our goal is to create a common mathematical framework (CMF) and an open agent architecture that allows one to research and explore alternative behavior models to add realism to software agents e.g., physiology and stress, personal values and emotive states, and cultural influences. Our CMF is based on a dynamical, game-theoretic approach to evolution and equilibria in Markov chains representing states of the world that the agents can act upon. In these worlds the agents' utilities (payoffs) are derived by a deep model of cognitive appraisal of intention achievement including assessment of emotional activation/decay relative to value hierarchies, and subject to (integrated) stress and related constraints. We present the progress to date on the mathematical framework, and on an environment for quickly editing opponents in terms of the various elements of the cognitive appraiser, utility generators, value hierarchies, and Markov chains. We illustrate the approach via an example training game for counter-terrorism and crowd management. Future research needs are elaborated including validity issues and ways to overcome the gaps in the behavioral literatures that confront developers of asymmetric forces.