# Learning Lifted STRIPS Models from Action Traces Alone: A Simple, General, and Scalable Solution

arXiv: https://arxiv.org/abs/2411.14995 · HTML: https://mlsanity.com/paper/2411.14995/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 22 Nov 2024.

## Builds on (most cited first, 5 of 5)

- [Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary](https://mlsanity.com/paper/1705.00154/) (2017)
- [Learning First-Order Symbolic Representations for Planning from the Structure of the State Space](https://mlsanity.com/paper/1909.05546/) (2019)
- [Unsupervised Grounding of Plannable First-Order Logic Representation from Images](https://mlsanity.com/paper/1902.08093/) (2019)
- [Learning First-Order Representations for Planning from Black-Box States: New Results](https://mlsanity.com/paper/2105.10830/) (2021)
- [Classical Planning in Deep Latent Space](https://mlsanity.com/paper/2107.00110/) (2021)

## Built on by (most cited first, 8 of 8)

- [Learning Lifted Action Models from Unsupervised Visual Traces](https://mlsanity.com/paper/2604.19043/) (2026)
- [Learning Lifted Action Models From Traces of Incomplete Actions and States](https://mlsanity.com/paper/2508.21449/) (2025)
- [From Next Token Prediction to (STRIPS) World Models](https://mlsanity.com/paper/2509.13389/) (2025)
- [On the Ability of Transformers to Verify Plans](https://mlsanity.com/paper/2603.19954/) (2026)
- [Autonomous Assessment of Generalizability of AI Agent Capabilities](https://mlsanity.com/paper/2512.16733/) (2025)
- [Differentiable Learning of Lifted Action Schemas for Classical Planning](https://mlsanity.com/paper/2605.13282/) (2026)
- [Learning Lifted Action Models from Traces with Minimal Information About Actions and States](https://mlsanity.com/paper/2605.18627/) (2026)
- [Learning to Search and Searching to Learn for Generalization in Planning](https://mlsanity.com/paper/2605.25720/) (2026)

## Abstract (arXiv)

Learning STRIPS action models from action traces alone is a challenging problem as it involves learning the domain predicates as well. In this work, a novel approach is introduced which, like the well-known LOCM systems, is scalable, but like SAT approaches, is sound and complete. Furthermore, the approach is general and imposes no restrictions on the hidden domain or the number or arity of the predicates. The new learning method is based on an efficient, novel test that checks whether the assumption that a predicate is affected by a set of action patterns, namely, actions with specific argument positions, is consistent with the traces. The predicates and action patterns that pass the test provide the basis for the learned domain that is then easily completed with preconditions and static predicates. The new method is studied theoretically and experimentally. For the latter, the method is evaluated on traces and graphs obtained from standard classical domains like the 8-puzzle, which involve hundreds of thousands of states and transitions. The learned representations are then verified on larger instances.