Lotinac D, Segovia-Aguas J, Jiménez S, Jonsson A. Automatic Generation of High-Level State Features for Generalized Planning. International Joint Conference on Artificial Intelligence. 2016
List of results published directly linked with the projects co-funded by the Spanish Ministry of Economy and Competitiveness under the María de Maeztu Units of Excellence Program (MDM-2015-0502).
List of publications acknowledging the funding in Scopus.
The record for each publication will include access to postprints (following the Open Access policy of the program), as well as datasets and software used. Ongoing work with UPF Library and Informatics will improve the interface and automation of the retrieval of this information soon.
The MdM Strategic Research Program has its own community in Zenodo for material available in this repository 
as well as at the UPF e-repository 
Lotinac D, Segovia-Aguas J, Jiménez S, Jonsson A. Automatic Generation of High-Level State Features for Generalized Planning. International Joint Conference on Artificial Intelligence. 2016
Lotinac D, Segovia-Aguas J, Jiménez S, Jonsson A. Automatic Generation of High-Level State Features for Generalized Planning. Proceedings of the 25th International Joint Conference on Artificial Intelligence; 2016 July 9-15; New York, United States. Palo Alto: AAAI Press; 2016. p. 3199-3205
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.
Additional material: