  Recent work on Neural-Symbolic systems that learn the discrete planning model
from images has opened a promising direction for expanding the scope of
Automated Planning and Scheduling to the raw, noisy data. However, previous
work only partially addressed this problem, utilizing the black-box neural
model as the successor generator. In this work, we propose Double-Stage Action
Model Acquisition (DSAMA), a system that obtains a descriptive PDDL action
model with explicit preconditions and effects over the propositional variables
unsupervized-learned from images. DSAMA trains a set of Random Forest
rule-based classifiers and compiles them into logical formulae in PDDL. While
we obtained a competitively accurate PDDL model compared to a black-box model,
we observed that the resulting PDDL is too large and complex for the
state-of-the-art standard planners such as Fast Downward primarily due to the
PDDL-SAS+ translator bottleneck. From this negative result, we argue that this
translator bottleneck cannot be addressed just by using a different, existing
rule-based learning method, and we point to the potential future directions.
