Machine learning approaches to knowledge compilation seek to improve the perfonnance of problem-solvers by storing solutions to previously solved problems in an efficient, generalized fonn. The problem-solver retrieves these learned solutions in appropriate later situations to obtain results more efficiently. However, by relying on its learned knowledge to provide a solution, the problem-solver may miss an alternative solution of higher quality - one that could have been generated using the original (non-learned) problem-solving knowledge. This phenomenon is referred to as the ITUlSking effect of learning.
In this paper, we examine a sequence of possible solutions for the masking effect. Each solution refines and builds on the previous one. The fmal solution is based on cascaded filters. When learned knowledge is retrieved, these filters alert the system about the inappropriateness of this knowledge so that the system can then derive a better alternative solution. We analyze conditions under which this solution will perfonn better than the others, and present experimental data supportivt: of the analysis. This investigation is based on a simulated robot domain called Groundworld.