Speed-Up Learning by Extracting Partial Structures of Explanations

Abstract

Explanation-Based Learning (EBL) has been used for speed-up learning in problem solving. Since there ary many combinations of macros in each explanation, EBL systems need a selective learning mechanism of macros. Some systems select macros that connects two peaks in a heuristic function. Another system employs heuristics that select useful macros. Although they work well in some domains, such methods depend on domain-dependent heuristics that have to be exploited by their users. This paper presents a heuristic-independent mechanism by detecting backtracking. The method uses a dead-end path as a negative explanation tree, compares it with positive one, and finds a first different node to remove its corresponding rule by composing a macro. Repeated substructures in such a macro are then combined by applying the generalization-to-N technique and by sharing common substructures. Experimental results in STRIPS domain show that, by selecting an appropriate set of macros, (1) backtracking in solving training examples are suppressed, (2) its problem solving efficiency does not deteriorate even after learning a number of examples, (3) after learning 30 training examples, no backtracking occurs in solving 100 test examples different from the training examples. In conclusion, the proposed method speeds up the problem solving from 10 to 100 times.