The CxBR Diffusion Engine - A tool for modeling humanbehavior on the battle field
Sven E. Eklund, Hans Fernlund, Avelino J. González · 2004
The option to automatically model the behavior of different actors during live exercise training would increase the value of the after-action-review (AAR) process. If a simulated model of the actors is available right after the live exercise training, the evaluation of their behavior would be more timely and alternative actions could also be evaluated at the same time. The CxBR Diffusion Engine merges technologies to establish a tool for automatic, on-line behavior modeling. Context Based Reasoning (CxBR) is a proven methodology to build simulated agents with human behavior. Genetic Programming (GP) provides the CxBR framework with learning capabilities to automatically create simulated agents with human behavior. The final piece in the CxBR Diffusion Engine is to provide an efficient, flexible, scaleable and mobile platform to evolve the agents’ behavior. This platform is the newly developed massively parallel architecture for distributed GP. The massively parallel architecture has the potential to execute the GP linear machine code representation at a rate of up to 50,000 generations per second. Implemented in an FPGA, this architecture is highly portable and applicable to mobile, on-line applications. This paper will present a theory on how the CxBR + GP can evolve simulated agents with human behavior by observation in a massively parallel architecture. These pieces will introduce all the necessary elements to build the CxBR Diffusion Engine that could model human behavior to enable individual AAR of trainees in the training field.