Optimization of human control strategy with simultaneously perturbed stochastic approximation

Modeling the dynamic human control strategy (HCS) is becoming an increasingly popular paradigm in a number of different research areas, ranging from robotics to intelligent vehicle highway systems. Usually, HCS models are derived empirically, rather than analytically, from real human input-output data. While these empirical models offer an effective means of transferring intelligent behaviors from humans to robots and other machines, the models are not explicitly optimized with respect to potentially important performance criteria. We therefore propose an iterative algorithm for optimizing an initially stable HCS model with respect to an independent, user-specified performance criterion. We first collect driving data from different individuals through a real-time graphic driving simulator. Next, we describe how we model each individual´s control strategy through flexible cascade neural networks. Once we have initially stable HCS models, we propose simultaneously perturbed stochastic approximation (SPSA) to optimize these models with respect to a chosen performance criterion. Finally, we describe and discuss some experimental results with the proposed algorithm