  We present a symbolic learning framework inspired by cognitive-like memory
functionalities (i.e., storing, retrieving, consolidating and forgetting) to
generate task representations to support high-level task planning and knowledge
bootstrapping. We address a scenario involving a non-expert human, who performs
a single task demonstration, and a robot, which online learns structured
knowledge to re-execute the task based on experiences, i.e., observations. We
consider a one-shot learning process based on non-annotated data to store an
intelligible representation of the task, which can be refined through
interaction, e.g., via verbal or visual communication. Our general-purpose
framework relies on fuzzy Description Logic, which has been used to extend the
previously developed Scene Identification and Tagging algorithm. In this paper,
we exploit such an algorithm to implement cognitive-like memory functionalities
employing scores that rank memorised observations over time based on simple
heuristics. Our main contribution is the formalisation of a framework that can
be used to systematically investigate different heuristics for bootstrapping
hierarchical knowledge representations based on robot observations. Through an
illustrative assembly task scenario, the paper presents the performance of our
framework to discuss its benefits and limitations.
