<div dir="ltr"><p dir="ltr" style="font-family:verdana,sans-serif;line-height:1.38;text-align:justify;margin-top:0pt;margin-bottom:0pt"><span style="color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Dear planning and learning enthusiasts,</span></p><p dir="ltr" style="font-family:verdana,sans-serif;line-height:1.38;text-align:justify;margin-top:12pt;margin-bottom:12pt"><span style="color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">We are pleased to invite you to attend the tutorial “</span><span style="color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Domain Model Learning in AI Planning</span><span style="color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">”, 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.</span></p><p dir="ltr" style="font-family:verdana,sans-serif;line-height:1.38;text-align:justify;margin-top:12pt;margin-bottom:12pt"><span style="color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">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.</span></p><p dir="ltr" style="font-family:verdana,sans-serif;line-height:1.38;text-align:justify;margin-top:12pt;margin-bottom:12pt"><span style="color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">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 </span><span style="color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">overview of state-of-the-art methods</span><span style="color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline"> for learning domain models under different assumptions (e.g., partially or noisy observability), to enable them to effectively use </span><span style="color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">open-source frameworks and tools</span><span style="color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline"> for domain-model learning, and highlight </span><span style="color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">open research challenges</span><span style="color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline"> at the intersection of machine learning and symbolic planning.  </span></p><p dir="ltr" style="font-family:verdana,sans-serif;line-height:1.38;text-align:justify;margin-top:12pt;margin-bottom:12pt"><span style="color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">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: </span></p><p dir="ltr" style="font-family:verdana,sans-serif;line-height:1.38;text-align:justify;margin-top:12pt;margin-bottom:12pt"><a href="https://domain-learning.github.io/" target="_blank" style="text-decoration-line:none"><span style="background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;text-decoration-line:underline;vertical-align:baseline">https://domain-learning.github.io</span></a></p><p dir="ltr" style="font-family:verdana,sans-serif;line-height:1.38;text-align:justify;margin-top:12pt;margin-bottom:12pt"><a href="https://aaai.org/conference/aaai/aaai-26/tutorial-and-lab-list/#th17" target="_blank" style="text-decoration-line:none"><span style="background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;text-decoration-line:underline;vertical-align:baseline">https://aaai.org/conference/aaai/aaai-26/tutorial-and-lab-list/#th17</span></a></p><p dir="ltr" style="font-family:verdana,sans-serif;line-height:1.38;text-align:justify;margin-top:12pt;margin-bottom:12pt"><br></p><p dir="ltr" style="font-family:verdana,sans-serif;line-height:1.38;text-align:justify;margin-top:12pt;margin-bottom:12pt"><span style="color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">We look forward to your participation in this AAAI-26 tutorial!</span></p><br style="font-family:verdana,sans-serif"><p dir="ltr" style="font-family:verdana,sans-serif;line-height:1.2;text-align:justify;margin-top:12pt;margin-bottom:0pt"><span style="color:rgb(0,0,0);background-color:transparent;font-style:italic;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Organizing Committee:</span></p><p dir="ltr" style="font-family:verdana,sans-serif;line-height:1.2;text-align:justify;margin-top:12pt;margin-bottom:0pt"><span style="color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Prof. Roni Stern, BGU</span></p><p dir="ltr" style="font-family:verdana,sans-serif;line-height:1.2;text-align:justify;margin-top:12pt;margin-bottom:0pt"><span style="color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Prof. Christian Muise, Queen’s University</span></p><p dir="ltr" style="font-family:verdana,sans-serif;line-height:1.2;text-align:justify;margin-top:12pt;margin-bottom:0pt"><span style="color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;vertical-align:baseline">Dr. Leonardo Lamanna, FBK</span></p></div>

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