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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}}</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|doi-access=free |hdl=10251/144560 |hdl-access=free }}</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|url-access=subscription }}</ref>
*Read more: [[Action model learning]]
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* [[Actor model]]
* [[Applications of artificial intelligence]]
* [[International Conference on Automated Planning and Scheduling]]▼
* [[Constraint satisfaction problem]]
▲* [[International Conference on Automated Planning and Scheduling]]
* [[Reactive planning]]
* [[Scheduling (computing)]]
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; Lists
* [[List of SMT solvers]]▼
* [[List of constraint programming languages]]
* [[List of emerging technologies]]
▲* [[List of SMT solvers]]
* [[Outline of artificial intelligence]]
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