Neural-Symbolic Descriptive Action Model from Images: The Search for STRIPS

Understand

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.