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

arXiv: https://arxiv.org/abs/1912.05492 · HTML: https://mlsanity.com/paper/1912.05492/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

## Built on by (most cited first, 1 of 1)

- [Classical Planning in Deep Latent Space](https://mlsanity.com/paper/2107.00110/) (2021)

## Abstract (arXiv)

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.