Proceedings PaperAdaptive explanation-based learning: a preliminary analysis of the utility of EBL
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Although a lot of research on explanation-based learning has been done, the adaptability of EBL to examples is no fully improved. In this paper, we propose a preliminary analysis for computing the utility of EBL in a logic programming environment. The research will contribute to the adaptive EBL as the theoretical criteria for selecting useful learned rules and removing useless rules. First, we define a whole EBL framework including both problem solving and learning. Next, the utility of EBL is defined as a utility function of two variables, the amount of test examples, and a probability distribution on test examples. Then we present a method to determine the utility function only by the trace of problem solving just on training examples, not text examples. By analyzing the utility function, we get the sufficient precondition of the EBL's utility and are able to determine whether the EBL system should learn. Finally we present the results on computing EBL's utility in examples. As a result, we have a very interesting fact that EBL deteriorates problem solving even in simple domain theory such Mitchell's SAFE-TO-STACK.