In many domains generalized plans can only/nbe computed if certain high-level state features,/ni.e. features that capture key concepts to accurately/ndistinguish between states and make good decisions,/nare available. In most applications of generalized/nplanning such features are hand-coded by/nan expert. This paper presents a novel method/nto automatically generate high-level state features/nfor solving a generalized planning problem. Our/nmethod extends a compilation of generalized planning/ninto classical planning and integrates the computation/nof generalized plans with the computation/nof features, in the form of conjunctive queries. Experiments/nshow that we generate features for diverse/ngeneralized planning problems and hence,/ncompute generalized plans without providing a/nprior high-level representation of the states. We/nalso bring a new landscape of challenging benchmarks/nto classical planning since our compilation/nnaturally models classification tasks as classical/nplanning problems.

This work is partially supported by grant TIN2015-67959 and the/nMaria de Maeztu Units of Excellence Programme MDM-2015-/n0502, MEC, Spain. Sergio Jimenez is partially supported by the /nJuan de la Cierva program funded by the Spanish government.