Modeling dynamic human control strategy (HCS) is becoming an
increasingly popular paradigm in a number of different research areas,
ranging from robotics to the intelligent vehicle highway
system. 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. In this paper, 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.
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