An Empirical Approach To Solving The General Utility Problem In Speedup Learning

Many recent approaches to avoiding the utility problem in speedup learning (the eventual degradation of performance due to increasing amounts of learned problem-solver control knowledge) rely on sophisticated utility measures and significant numbers of training problems to accurately estimate the utility of control knowledge. Empirical results presented here and elsewhere indicate that a simple selection strategy of retaining all control rules derived from a training problem solution quickly defines an efficient set of control knowledge from few training problems. This simple selection strategy provides a low-cost alternative to example-intensive approaches for improving the speed of a problem solver. Experimentation illustrates the existence of a minimum (representing least cost) in the learning curve which is reached after a few training examples. Stress is placed on controlling the amount of learned knowledge as opposed to which knowledge. An attempt is also made to relate domain ch..