Automatic Generation of High-Level State Features for Generalized Planning

In many domains generalized plans can only be computed if certain high-level state features, i.e. features that capture key concepts to accurately distinguish between states and make good decisions, are available. In most applications of generalized planning such features are hand-coded by an expert. This paper presents a novel method to automatically generate high-level state features for solving a generalized planning problem. Our method extends a compilation of generalized planning into classical planning and integrates the computation of generalized plans with the computation of features, in the form of conjunctive queries. Experiments show that we generate features for diverse generalized planning problems and hence, compute generalized plans without providing a prior high-level representation of the states. We also bring a new landscape of challenging benchmarks to classical planning since our compilation naturally models classification tasks as classical planning problems.