In this paper, we address the problem of transferring human control
strategies (HCS) from an expert model to an apprentice model. The
proposed algorithm allows us to develop useful apprentice models that
nevertheless incorporate some of the robust aspects of the expert HCS
models. We first describe our experimental platform - a real-time
graphich driving simulator - for collecting and modeling human control
strategies. Then, we discuss an adaptive neural network learning
architecture for abstracting HCS models. Next, we define a hidden
Markov model (HMM) based similarity measure which allows us to compare
different human control strategies. This similarity measure is
combined subsequently with simultaneously perturbed stochastic
approximation to develop our proposed transfer learning algorithm. In
this algorithm, an expert HCS model influences both the structure and
the parametric representation of the eventual apprentice HCS
model. Finally, we describe some experimental results of the proposed
algorithm.
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