Human control strategy: abstraction, verification, and replication

Abstract

In this article, we describe and develop methodologies for modeling and transferring human control strategy. This research has potential application in a variety of areas such as the intelligent vehicle highway system, 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.