Invitation to the AAAI-26 Tutorial “Domain Model Learning in AI Planning”
Leonardo Lamanna
Dear planning and learning enthusiasts,
We are pleased to invite you to attend the tutorial “Domain Model Learning in AI Planning”, to be held at AAAI 2026 in Singapore EXPO on Wednesday, January 21st (half-day), which covers the foundations, recent advances in techniques and tools, and open challenges for automated domain-model learning.
Automated planning heavily relies on the availability of planning domain models that describe the environment dynamics. However, handcrafting such models is widely known to be time-consuming, prone to errors, and to require extensive knowledge about the environments. For these reasons, the AI planning community has proposed several theories and algorithms that automatically learn domain models.
The tutorial is aimed at researchers, practitioners, and students interested in techniques for learning planning domain models. The objective of this tutorial is to give participants a clear overview of state-of-the-art methods for learning domain models under different assumptions (e.g., partially or noisy observability), to enable them to effectively use open-source frameworks and tools for domain-model learning, and highlight open research challenges at the intersection of machine learning and symbolic planning.
If you have any questions, please do not hesitate to contact us. Further details about the tutorial are available on the tutorial and AAAI-2026 websites:
https://domain-learning.github.io
https://aaai.org/conference/aaai/aaai-26/tutorial-and-lab-list/#th17
We look forward to your participation in this AAAI-26 tutorial!
Organizing Committee:
Prof. Roni Stern, BGU
Prof. Christian Muise, Queen’s University
Dr. Leonardo Lamanna, FBK