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

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    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..
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