ABSTRACT
Our goal is to develop a hybrid cognitive model of how humans acquire skills on complex cognitive tasks. We are pursuing this goal by designing hybrid computational architectures for the NRL Navigation task, which requires competent sensorimotor coordination. In this paper, we describe results of directly fitting human execution data on this task. We next present and then empirically compare two methods for modeling control knowledge acquisition (reinforcement learning and a novel variant of action models) with human learning on the task. The paper concludes with an experimental demonstration of the impact of background knowledge on system performance. Our results indicate that the performance of our action models approach more closely approximates the rate of human learning on this task than does reinforcement learning.
