Constructive Logic and Behavior Engine for Efficient and Responsive Training (COLBERT)

ABSTRACT: Training simulations that currently support Close Air Support (CAS) training and the Joint Terminal Attack Controller (JTAC) training in particular, are laborious to configure and expensive to manage with live personnel, which results in training that is limited in scope. For example, current simulations, like the Indirect Fire Forward Observer Trainer (I-FACT), require numerous observer/controllers to partake as"White Force"players in the simulation, driving up the manpower cost of conducting JTAC training. To fill the roles, communications, and behaviors of White Force participants in simulation-based training environments, Charles River Analytics proposes to design and demonstrate a Constructive Logic and Behavior Engine for Efficient and Responsive Training (COLBERT). COLBERT provides a graphical development and run-time environment to construct and execute CAS-related behavior models. We will base COLBERT on AgentWorks, our in-house, intelligent agent platform, coupled with new experience management tools that support Trainers across the four phases of training development (planning, preparation, execution, and assessment) to ensure an effective training experience. BENEFIT: COLBERT will provide immediate and tangible benefits to training developers by supporting effective and cost-efficient training across a wide range of domains and simulation environments, including the Joint Terminal Attack Controller Training Rehearsal System being developed at AFRL. In addition, COLBERT will provide considerable benefit for other simulation-based training, including flight and maintenance space simulation training. We also plan to enhance our user-friendly agent development environment, AgentWorks, with training capabilities that will increase its appeal in both the simulation-based training and entertainment (e.g., video games) markets.