# MACQ: A Holistic View of Model Acquisition Techniques

arXiv: https://arxiv.org/abs/2206.06530 · HTML: https://mlsanity.com/paper/2206.06530/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 14 Jun 2022.

## Builds on (most cited first, 6 of 6)

- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2004.07180/) (2020)
- [Learning First-Order Representations for Planning from Black-Box States: New Results](https://mlsanity.com/paper/2105.10830/) (2021)
- [Learning Partially Observable Deterministic Action Models](https://mlsanity.com/paper/1401.3437/) (2014)
- [A Case for Backward Compatibility for Human-AI Teams](https://mlsanity.com/paper/1906.01148/) (2019)
- [Learning Action Models from Disordered and Noisy Plan Traces](https://mlsanity.com/paper/1908.09800/) (2019)
- [MiniConf -- A Virtual Conference Framework](https://mlsanity.com/paper/2007.12238/) (2020)

## Built on by (most cited first, 7 of 7)

- [Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning](https://mlsanity.com/paper/2305.14909/) (2023)
- [Can LLMs Fix Issues with Reasoning Models? Towards More Likely Models for AI Planning](https://mlsanity.com/paper/2311.13720/) (2023)
- [Egocentric Planning for Scalable Embodied Task Achievement](https://mlsanity.com/paper/2306.01295/) (2023)
- [Planning Domain Model Acquisition from State Traces without Action Parameters](https://mlsanity.com/paper/2402.10726/) (2024)
- [On Automating Video Game Regression Testing by Planning and Learning](https://mlsanity.com/paper/2402.12393/) (2024)
- [Reducing Human-Robot Goal State Divergence with Environment Design](https://mlsanity.com/paper/2404.15184/) (2024)
- [Enabling Robots to Identify Missing Steps in Robot Tasks for Guided Learning from Demonstration](https://mlsanity.com/paper/2311.18355/) (2023)

## Abstract (arXiv)

For over three decades, the planning community has explored countless methods for data-driven model acquisition. These range in sophistication (e.g., simple set operations to full-blown reformulations), methodology (e.g., logic-based vs. planing-based), and assumptions (e.g., fully vs. partially observable). With no fewer than 43 publications in the space, it can be overwhelming to understand what approach could or should be applied in a new setting. We present a holistic characterization of the action model acquisition space and further introduce a unifying framework for automated action model acquisition. We have re-implemented some of the landmark approaches in the area, and our characterization of all the techniques offers deep insight into the research opportunities that remain; i.e., those settings where no technique is capable of solving.