William G. Kennedy and Kenneth A. De Jong
Much of the work in machine learning has focused on demonstrating the efficacy of learning techniques using training and testing phases. On-line learning over the long term places different demands on symbolic machine learning techniques and raises a different set of questions for symbolic learning than for empirical learning. We have instrumented Soar to collect data and characterize the long-term learning behavior of Soar and demonstrate an effective approach to the utility problem. In this paper we describe our approach and provide results.