Content deleted Content added
Line 80:
{{See also|Sussman anomaly}}
===Model Acquisition===
Creating ___domain models is difficult, takes a lot of time, and can easily lead to mistakes. To help with this, several methods have been developed to automatically learn full or partial ___domain models from given observations.
<ref>{{cite conference |author=Callanan, Ethan and De Venezia, Rebecca and Armstrong, Victoria and Paredes, Alison and Chakraborti, Tathagata and Muise, Christian |title=MACQ: A Holistic View of Model Acquisition Techniques |conference=ICAPS Workshop on Knowledge Engineering for Planning and Scheduling (KEPS) |year=2022 |url=https://icaps22.icaps-conference.org/workshops/KEPS/KEPS-22_paper_4962.pdf |access-date=2025-06-10 |url-status=live}}</ref>
<ref>{{cite journal |author=Aineto, Diego and Jiménez Celorrio, Sergio and Onaindia, Eva |title=Learning action models with minimal observability |journal=Artificial Intelligence |volume=275 |pages=104–137 |year=2019 |doi=10.1016/j.artint.2019.05.003 |url=https://doi.org/10.1016/j.artint.2019.05.003 |access-date=2025-06-10 |url-status=live}}</ref>
<ref>{{cite journal |author=Jiménez, Sergio and de la Rosa, Tomás and Fernández, Susana and Fernández, Fernando and Borrajo, Daniel |title=A review of machine learning for automated planning |journal=The Knowledge Engineering Review |volume=27 |issue=4 |pages=433–467 |year=2012 |doi=10.1017/S026988891200001X |url=https://doi.org/10.1017/S026988891200001X |access-date=2025-06-10 |url-status=live}}</ref>
*Read more: [[Action model learning]]
=== Reduction to other problems ===
| |||