The problem of specifying high-level knowledge bases for planning becomes a
hard task in realistic environments. This knowledge is usually handcrafted and
is hard to keep updated, even for system experts. Recent approaches have shown
the success of classical planning at synthesizing action models even when all
intermediate states are missing. These approaches can synthesize action schemas
in Planning Domain Definition Language (PDDL) from a set of execution traces
each consisting, at least, of an initial and final state. In this paper, we
propose a new algorithm to unsupervisedly synthesize STRIPS action models with
a classical planner when action signatures are unknown. In addition, we
contribute with a compilation to classical planning that mitigates the problem
of learning static predicates in the action model preconditions, exploits the
capabilities of SAT planners with parallel encodings to compute action schemas
and validate all instances. Our system is flexible in that it supports the
inclusion of partial input information that may speed up the search. We show
through several experiments how learned action models generalize over unseen
planning instances.Comment: Presented to Genplan 2020 workshop, held in the AAAI 2020 conference
(https://sites.google.com/view/genplan20) (2021/03/05: included missing
acknowledgments
