Applying Inductive Program Synthesis to Macro Learning

The goal of this paper is to demonstrate that inductive progrwn synthesis can be applied to learning macrooperators 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.