Applying inductive program synthesis to macro learning
Ute Schmid, Fritz Wysotzki · 2000
The goal of this paper is to demonstrate that inductive program 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. Introduction Interest in learning macro-operators for planning (Minton 1985; Korf 1985) has decreased over the last decade, mainly because of the utility problem (Minton 1985). But new results in reinforcement learning are promising -- showing that more complex problems are solvable and that pla...