Comunicació presentada a la Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI 2017), celebrada els dies 19 a 25 d'agost a Melbourne, Austràlia.

Classical planning is concerned with problems where a goal needs to be reached from a known initial state by doing actions with deterministic, known effects. Classical planners, however, deal only with classical problems that can be expressed in declarative planning languages such as STRIPS or PDDL. This prevents their use on problems that are not easy to model declaratively or whose dynamics are given via simulations. Simulators do not provide a declarative representation of actions, but simply return successor states. The question we address in this paper is: can a planner that has access to the structure of states and goals only, approach the performance of planners that also have access to the structure of actions expressed in PDDL? To answer this, we develop domain-independent, black box planning algorithms that completely ignore action structure, and show that they match the performance of state-of-the-art classical planners on the standard planning benchmarks. Effective black box algorithms open up new possibilities for modeling and for expressing control knowledge, which we also illustrate.

G. Francès is supported by the M. de Maeztu Programme (MDM-2015-0502) and H. Geffner by grant TIN2015-67959-P, both from MINECO, Spain. M. Ramirez and N. Lipovetzky have been partially funded by the Australian DST Group.