Papers › MACQ: A Holistic View of Model Acquisition Techniques
MACQ: A Holistic View of Model Acquisition Techniques
Ethan Callanan, Rebecca De Venezia, Victoria Armstrong, Alison Paredes, Tathagata Chakraborti, Christian Muise
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.
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