These behavior networks are composed of basic building blocks we call basis behaviors . Such systems will learn to reorganize based on the environment and the task specification. Throughout the network we propose to build in analytic redundancy and hence fault tolerance. Our method has low computation and bandwidth requirements and scales linearly with population. The method is insensitive to individual differences and removes individual vs. group conflict.
Primitives are forward models for visual recognition, segmentation, and prediction.
After imitating, the reconstruction error facilitates learning a better match. Repetition and training creates new basis behavior primitives. Design of autonomous take off and landing behavior for the robotic helicopter. Hand-construction of behavior networks that can perform specific missions as well as preliminary work on automatic generation of such networks using behavior abstraction. The multi-target tracking technology is being exported in part to the Office of Naval Research and Sandia National Laboratories.