2 Inductively Speeding Up Logic Programs

This paper presents a speed-up learning method for logic programs, which accelerates a program by composing macro clauses based on partial structures of proof trees. Many systems have been proposed for composing useful macros, e.g., some of them select macros that connects two peaks in a heuristic function. Another 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. We propose a heuristic-independent mechanism by detecting backtrack-ing. The method uses a dead-end path as a negative explanation tree, compares it with positive one, and nds a rst dierent node to remove its corresponding rule by composing a macro. Repeated substructures in such a macro are then combined by applying the generalize-number 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 eciency does not deteriorate even after learning a number of examples, 3) after learning 30 training examples, no backtracking occurs in solving 100 test examples dierent from the training examples. In conclusion, the proposed method speeds up the problem solving from 10 to 100 times.