Automating the CGF Model Development and Refinement Process by Observing Expert Behavior in a Simulation

1. Abstract Ultimate widespread use of CGF entities in tactical simulations will depend on how easy it will be to develop, refine and maintain models of the behaviors to be represented. However, proper vehicle behavior model development for CGF applications can be difficult as well as expensive. Means to quickly and effectively create models for new vehicles and/or behaviors must be developed to permit CGF models to be widely used in the future. One realistic approach to overcoming this model generation bottleneck is to create and refine vehicle models through automated observation of the behavior of an entity being controlled by a human expert in a simulation. This is a learning paradigm quite commonly used by humans. This paper describes an on-going research effort that introduces some new ideas on how to accomplish autonomous model development through observation.