C2I Surrogate Behaviors

"Warfighter decision-making is increasingly being pushed down to lower-levels ofcommand in search of better and more rapid response. This increases requirements forsimulation-based assessment, training, and mission rehearsal. Unfortunately, the C2Imodeling of both adversary and friendly forces in simulation is poor, requiring too muchlabor to set up and run simulations. Process-based AI approaches are both labor-intensive and can produce unrealistic models. Improved cognitive modeling, especiallywhen capable of learning, holds promise to address the C2I modeling problem, enablingbetter simulation-based assessment, training, and mission rehearsal, without prohibitivelyhigh labor costs. The combined Phase I/II approach builds upon previous SBIR-fundedR&D integrating Behavior-Based Robotics (BBR) technology with emerging 4thgeneration CGF systems, and it proposes to leverage emerging BBR learning techniquesto produce believable, unpredictable, adaptable C2I surrogates. This provides acombination of leading-edge BBR technology integrated with commercial-grade CGFtechnology. In this effort, we will produce a Functional Analysis of the Proposed System,providing a distillation of functional requirements. We will survey the emergingtechniques for learning in BBR systems and evaluate each against our design goals andfunctional requirements. Finally, we will produce a Comprehensive SystemsDescription to include a system design. The propo