MACQ: A Holistic View of Model Acquisition Techniques
Abstract
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.
Cite
@article{arxiv.2206.06530,
title = {MACQ: A Holistic View of Model Acquisition Techniques},
author = {Ethan Callanan and Rebecca De Venezia and Victoria Armstrong and Alison Paredes and Tathagata Chakraborti and Christian Muise},
journal= {arXiv preprint arXiv:2206.06530},
year = {2022}
}
Comments
8 pages, 7 figures, KEPS Workshop Submission