Applying Inductive Program Synthesis to Macro Learning

摘要

The goal of this paper is to demonstrate that inductive program synthesis can be applied to learning macro-operators from planning experience. We define macros as recursive program schemes (RPSs). An RPS represents the complete subgoal structure of a given problem domain with arbitrary complexity (e. g., rocket transportation problem with n objects), that is, it represents domain specific control knowledge. We propose the following steps for macro learning: (1) Exploring a problem domain with small complexity (e. g., rocket with 3 objects) using an universal planning technique, (2) transforming the universal plan into a finite program, and (3) generalizing this program into an RPS.