On learning discontinuous human control strategies

Abstract

Models of human control strategy (HCS), which accurately emulate dynamic human behavior, have far reaching potential in areas ranging from robotics to virtual reality to the intelligent vehicle highway project. A number of learning algorithms, including fuzzy logic, neural networks, and locally weighted regression exist for modeling continuous human control strategies. These algorithms, however, may not be well suited for modeling discontinuous human control strategies. Therefore, we propose a new stochastic, discontinuous modeling framework, for abstracting human control strategies, based on hidden Markov models (HMM). In this paper, we first describe the real-time driving simulator which we developed for investigating human control strategies. Next, we demonstrate the shortcomings of a typical continuous modeling approach in modeling discontinuous human control strategies. We then propose an HMM-based method for modeling discontinuous human control strategies. The proposed controller overcomes these shortcomings and demonstrates greater fidelity to the human training data. We conclude the paper with further comparisons between the two competing modeling approaches and we propose avenues for future research. © 2001 John Wiley & Sons, Inc.