In this article, we describe and develop methodologies for modeling
and transferring human control strategy (HCS). This research has
potential application in a variety of areas such as the Intelligent
Vehicle Highway System (IVHS), human-machine interfacing, real-time
training, space telerobotics and agile manufacturing. We specifically
address the following issues: (1) how to efficiently model human
control strategy through learning cascade neural networks, (2) how to
select state inputs in order to generate reliable models, (3) how to
validate the computed models through an independent, Hidden Markov
Model-based procedure, and (4) how to effectively transfer human
control strategy. We have implemented this approach experimentally in
the real-time control of a human driving simulator, and are working to
transfer these methodologies for the control of an autonomous vehicle
and a mobile robot. In providing a framework for abstracting
computational models of human skill, we expect to facilitate analysis
of human control, the development of human-like intelligent machines,
improved human-robot coordination, and the transfer of skill from one
human to another.
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