Stochastic similarity for validating human control strategy models

Abstract

Modeling dynamic human control strategy (HCS), or human skill in response to real-time sensing is becoming an increasingly popular paradigm in many different research areas. We propose a stochastic similarity measure, based on hidden Markov model analysis, capable of comparing and contrasting stochastic, dynamic, multidimensional trajectories. We first derive and demonstrate properties of the similarity measure for stochastic systems. We then apply the similarity measure to real-time human driving data by comparing different control strategies among different individuals. We show that the proposed similarity measure out performs the more traditional Bayes classifier in correctly grouping driving data from the same individual. Finally, we illustrate how the similarity measure can be used in the validation of models which are learned from experimental data, and how we can connect model validation and model learning to iteratively improve our models of HCS.