  In symbolic planning systems, the knowledge on the domain is commonly
provided by an expert. Recently, an automatic abstraction procedure has been
proposed in the literature to create a Planning Domain Definition Language
(PDDL) representation, which is the most widely used input format for most
off-the-shelf automated planners, starting from `options', a data structure
used to represent actions within the hierarchical reinforcement learning
framework. We propose an architecture that potentially removes the need for
human intervention. In particular, the architecture first acquires options in a
fully autonomous fashion on the basis of open-ended learning, then builds a
PDDL domain based on symbols and operators that can be used to accomplish
user-defined goals through a standard PDDL planner.
  We start from an implementation of the above mentioned procedure tested on a
set of benchmark domains in which a humanoid robot can change the state of some
objects through direct interaction with the environment. We then investigate
some critical aspects of the information abstraction process that have been
observed, and propose an extension that mitigates such criticalities, in
particular by analysing the type of classifiers that allow a suitable grounding
of symbols.
