A heuristic approach to the discovery of macro-operators

Abstract

This paper describes a heuristic approach to the discovery of useful macro-operators (macros) in problem solving. The approach has been implemented in a program,Maclearn, that has three parts: macro-proposer, static filter, and dynamic filter. Learning occurs during problem solving, so that performance unproves in the course of a single problem trial. Primitive operators and macros are both represented within a uniform representational framework that is closed under composition. This means that new macros can be defined in terms of others, which leads to a definitional hierarchy. The representation also supports the transfer of macros to related problems.Maclearn is embedded in a supporting system that carries out best-first search. Experiments in macro learning were conducted for two classes of problems: peg solitaire (generalized “Hi-Q puzzle”), and tile sliding (generalized “Fiteen puzzle”). The results indicate thatMaclearn's filtering heuristics all improve search performance, sometimes dramatically. When the system was given practice on simpler training problems, it learned a set of macros that led to successful solutions of several much harder problems.