# Learning STRIPS Action Models with Classical Planning

arXiv: https://arxiv.org/abs/1903.01153 · HTML: https://mlsanity.com/paper/1903.01153/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2019.

## Built on by (most cited first, 14 of 14)

- [CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization](https://mlsanity.com/paper/2310.10134/) (2023)
- [Learning Symbolic Operators for Task and Motion Planning](https://mlsanity.com/paper/2103.00589/) (2021)
- [Grammar Prompting for Domain-Specific Language Generation with Large Language Models](https://mlsanity.com/paper/2305.19234/) (2023)
- [Classical Planning in Deep Latent Space](https://mlsanity.com/paper/2107.00110/) (2021)
- [Learning Efficient Abstract Planning Models that Choose What to Predict](https://mlsanity.com/paper/2208.07737/) (2022)
- [Tapas Are Free! Training-Free Adaptation of Programmatic Agents via LLM-Guided Program Synthesis in Dynamic Environments](https://mlsanity.com/paper/2508.11425/) (2025)
- [Safe Learning of PDDL Domains with Conditional Effects -- Extended Version](https://mlsanity.com/paper/2403.15251/) (2024)
- [KGLAMP: Knowledge Graph-guided Language model for Adaptive Multi-robot Planning and Replanning](https://mlsanity.com/paper/2602.04129/) (2026)
- [D-VAL: An automatic functional equivalence validation tool for planning domain models](https://mlsanity.com/paper/2104.14602/) (2021)
- [Planning for Learning Object Properties](https://mlsanity.com/paper/2301.06054/) (2023)
- [Scale-Plan: Scalable Language-Enabled Task Planning for Heterogeneous Multi-Robot Teams](https://mlsanity.com/paper/2603.08814/) (2026)
- [Precondition and Effect Reasoning for Action Recognition](https://mlsanity.com/paper/2112.10057/) (2021)
- [Optimistic Reinforcement Learning-Based Skill Insertions for Task and Motion Planning](https://mlsanity.com/paper/2510.14065/) (2025)
- [Macro-Operator Generation and Predicate Selection for TAMP Operator Learning](https://mlsanity.com/paper/2608.23629/) (2026)

## Abstract (arXiv)

This paper presents a novel approach for learning STRIPS action models from examples that compiles this inductive learning task into a classical planning task. Interestingly, the compilation approach is flexible to different amounts of available input knowledge; the learning examples can range from a set of plans (with their corresponding initial and final states) to just a pair of initial and final states (no intermediate action or state is given). Moreover, the compilation accepts partially specified action models and it can be used to validate whether the observation of a plan execution follows a given STRIPS action model, even if this model is not fully specified.