Human cognition can be viewed as a non-linear dynamical system receiving inputs and produces outputs that vary over time. Chaos theory describes the behavior of certain non-linear dynamic systems that may appear to proceed according to chance even though their behavior is determined by precise laws and is highly sensitive to initial conditions. Tom Clarke and his research group are studying the effectiveness of using chaos theory to measure and evaluate human and robot performance, but the approximations used are performed off-line due to their data and computational time requirements.
The NCCS Laboratory is assisting in this research by exploring ways in which transfer learning can be used to make the computational effort of these measures more manageable in real time. Our approach is to use traditional machine learning methods on existing data in order to determine general baseline parameters that yield fast and accurate approximations, then tune these parameters in real time on specific, novel sequences in order to improve computational speed. The goal is to be able to produce timely, on-line performance feedback for agents in the system.