<div dir="ltr"><div style="border-top:none;border-right:none;border-left:none;border-bottom:1pt none windowtext;padding:0in 0in 10pt">[Apologies for cross-posting]<p class="MsoNormal" style="margin:10pt 0in 0in;line-height:20.0933px;border:none;padding:0in;font-size:11pt;font-family:Arial,sans-serif"><b><u><span lang="EN" style="font-size:12pt;line-height:21.92px">GenPlan '23: NeurIPS 2023 Workshop on Generalization in Planning<br></span></u></b><span lang="EN"><a href="https://urldefense.com/v3/__https://aair-lab.github.io/genplan23__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpwAScV1g$" target="_blank"><span style="font-size:12pt;line-height:21.92px">https://aair-lab.github.io/genplan23</span></a></span><u><span lang="EN" style="font-size:12pt;line-height:21.92px"></span></u></p></div><p class="MsoNormal" style="margin:0in;text-align:justify;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><b><span lang="EN">TL; DR:</span></b><span lang="EN"> </span><span lang="EN" style="font-size:10.5pt;line-height:16.1px;font-family:Roboto;color:rgb(68,71,70);background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial">This NeurIPS workshop will feature recent research on generalization and transfer in all forms of sequential decision making. Please consider submitting your recent work as well as surveys highlighting your recent results by September 29, 2023 at </span><span lang="EN"><a href="https://urldefense.com/v3/__https://bit.ly/SubmitToGenPlan23__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpywrMdXI$" target="_blank"><span style="font-size:10.5pt;line-height:16.1px;font-family:Roboto;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial">https://bit.ly/SubmitToGenPlan23</span></a></span><span lang="EN" style="font-size:10.5pt;line-height:16.1px;font-family:Roboto;color:rgb(68,71,70);background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial">. </span><span lang="EN"><a href="https://urldefense.com/v3/__https://neurips.cc/Conferences/2023__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpq6mwgNE$" target="_blank"><span style="font-size:10.5pt;line-height:16.1px;font-family:Roboto;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial">NeurIPS 2023</span></a></span><span lang="EN" style="font-size:10.5pt;line-height:16.1px;font-family:Roboto;color:rgb(68,71,70);background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"> will be held in New Orleans, December 10-16, 2023. Details below!</span><span lang="EN"></span></p><p class="MsoNormal" style="margin:0in;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN"> </span></p><p class="MsoNormal" style="margin:0in;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><b><span lang="EN">CONFIRMED INVITED SPEAKERS</span></b><b><span lang="EN" style="font-size:8pt;line-height:12.2667px"></span></b></p><p class="MsoNormal" style="margin:0in;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN"> </span></p><p class="MsoNormal" style="margin:0in;line-height:22px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN"><a href="https://urldefense.com/v3/__https://feryal.github.io/__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpuJc4ljg$" target="_blank">Feryal Behbahani</a>, Google DeepMind</span></p><p class="MsoNormal" style="margin:0in;line-height:22px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN"><a href="https://urldefense.com/v3/__https://rraileanu.github.io/__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpLt-eGWU$" target="_blank">Roberta Raileanu</a>, Meta AI</span></p><p class="MsoNormal" style="margin:0in;line-height:22px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN"><a href="https://urldefense.com/v3/__https://amyzhang.github.io/__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOp8xfhGiQ$" target="_blank">Amy Zhang</a>, The University of Texas at Austin, USA and Meta AI</span></p><p class="MsoNormal" style="margin:0in;line-height:22px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN"><a href="https://urldefense.com/v3/__https://www.diag.uniroma1.it/*degiacom/__;fg!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpzZWuwRc$" target="_blank">Giuseppe De Giacomo</a>, University of Oxford, United Kingdom</span></p><p class="MsoNormal" style="margin:0in;line-height:22px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN"><a href="https://urldefense.com/v3/__https://www-i6.informatik.rwth-aachen.de/*hector.geffner/__;fg!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOp52YYMZ8$" target="_blank">Hector Geffner</a>, RWTH Aachen University, Germany and Linköping University, Sweden</span></p><p class="MsoNormal" style="margin:0in;line-height:22px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN"><a href="https://urldefense.com/v3/__https://www.cs.utexas.edu/*pstone/__;fg!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpEJFQbzk$" target="_blank">Peter Stone</a>, The University of Texas at Austin, USA and Sony AI</span></p><div style="border-top:none;border-right:none;border-left:none;border-bottom:1pt none windowtext;padding:0in 0in 10pt"><p class="MsoNormal" style="margin:0in;line-height:20.0933px;border:none;padding:0in;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN"> </span></p></div><div style="border-top:1pt none windowtext;border-bottom:1pt none windowtext;border-right-style:none;border-left-style:none;padding:10pt 0in"><p class="MsoNormal" style="margin:0in;line-height:20.0933px;border:none;padding:0in;font-size:11pt;font-family:Arial,sans-serif"><b><span lang="EN">WORKSHOP OVERVIEW</span></b></p></div><div style="border-top:none;border-right:none;border-left:none;border-bottom:1pt none windowtext;padding:0in 0in 10pt"><p class="MsoNormal" style="margin:0in;text-align:justify;border:none;padding:0in;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">Humans are good at solving sequential decision-making problems, generalizing from a few examples, and learning skills that can be transferred to solve unseen problems. However, these problems remain long-standing open problems in AI.</span></p><p class="MsoNormal" style="margin:0in;text-align:justify;border:none;padding:0in;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN"> </span></p><p class="MsoNormal" style="margin:0in;text-align:justify;border:none;padding:0in;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">This workshop will feature a synthesis of the best ideas on the topic from multiple highly active research communities. On the one hand, recent advances in deep-reinforcement learning have led to data-driven methods that provide strong short-horizon reasoning and planning, with open problems regarding sample efficiency, generalizability and transferability. On the other hand, advances and open questions in the AI planning community have been complementary, featuring robust analytical methods that provide sample-efficient generalizability and transferability for long-horizon sequential decision making, with open problems in short-horizon control and in the design and modeling of representations.</span></p><p class="MsoNormal" style="margin:0in;text-align:justify;border:none;padding:0in;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN"> </span></p><p class="MsoNormal" style="margin:0in;text-align:justify;border:none;padding:0in;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">We welcome submissions addressing the problem of generalizable and transferable learning in all forms of sequential decision making. This event represents the seventh edition of the recurring <a href="https://urldefense.com/v3/__http://www.genplan.ai/__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOp6eeVvyE$" target="_blank">GenPlan</a> series of Workshops.</span></p><p class="MsoNormal" style="margin:0in;text-align:justify;line-height:20.0933px;border:none;padding:0in;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN"> </span></p><p class="MsoNormal" style="margin:0in;line-height:normal;border:none;padding:0in;font-size:11pt;font-family:Arial,sans-serif"><b><span lang="EN">TOPICS</span></b></p></div><p class="MsoNormal" style="margin:0in;text-align:justify;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">The workshop will focus on research related to all aspects of learning, generalization, and transfer in sequential decision-making (SDM). This topic features technical problems that are of interest not only in multiple subfields of AI research (including reinforcement learning, automated planning, and learning for knowledge representation) but also in other fields of research, including formal methods and program synthesis. We will welcome submissions that address <b>formal</b> as well as <b>empirical</b> issues on topics such as:</span></p><p class="MsoNormal" style="margin:0in;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN"> </span></p><p class="MsoNormal" style="margin:0in 0in 0in 47pt;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">●<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">      </span></span><span lang="EN">Formulations of generalized SDM problems.</span></p><p class="MsoNormal" style="margin:0in 0in 0in 47pt;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">●<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">      </span></span><span lang="EN">Representations, learning and synthesis for generalized plans and policies.</span></p><p class="MsoNormal" style="margin:0in 0in 0in 47pt;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">●<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">      </span></span><span lang="EN">Learning for transfer and generalization in reinforcement learning.</span></p><p class="MsoNormal" style="margin:0in 0in 0in 47pt;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">●<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">      </span></span><span lang="EN">Learning and representing hierarchical policies and behaviors for SDM.</span></p><p class="MsoNormal" style="margin:0in 0in 0in 47pt;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">●<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">      </span></span><span lang="EN">Learning and synthesis of generalizable solutions for SDM problem classes.</span></p><p class="MsoNormal" style="margin:0in 0in 0in 47pt;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">●<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">      </span></span><span lang="EN">Learning paradigms, representations, and algorithms for transferring learned knowledge and solutions to new SDM problems.</span></p><p class="MsoNormal" style="margin:0in 0in 0in 47pt;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">●<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">      </span></span><span lang="EN">Learning and representing generalized Q/V functions and heuristics for plan and policy generalization.</span></p><p class="MsoNormal" style="margin:0in 0in 0in 47pt;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">●<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">      </span></span><span lang="EN">Learning high-level models and hierarchical solutions for generalizable SDM.</span></p><p class="MsoNormal" style="margin:0in 0in 0in 47pt;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">●<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">      </span></span><span lang="EN">Neuro-symbolic approaches for generalization and transfer in SDM.</span></p><p class="MsoNormal" style="margin:0in 0in 0in 47pt;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">●<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">      </span></span><span lang="EN">Few-shot learning and transfer for SDM.</span></p><p class="MsoNormal" style="margin:0in 0in 0in 47pt;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">●<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">      </span></span><span lang="EN">Meta-learning for generalizable policies.</span></p><p class="MsoNormal" style="margin:0in 0in 0in 47pt;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">●<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">      </span></span><span lang="EN">Learning for program synthesis.</span></p><p class="MsoNormal" style="margin:0in 0in 0in 47pt;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">●<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">      </span></span><span lang="EN">Learning domain control knowledge and partial policies.</span></p><p class="MsoNormal" style="margin:0in 0in 0in 47pt;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">●<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">      </span></span><span lang="EN" style="font-size:11.5pt;line-height:17.6333px;color:rgb(29,28,29);background:rgb(248,248,248)">Generalization and transfer in robot planning problems.</span><span lang="EN"></span></p><p class="MsoNormal" style="margin:0in 0in 0in 47pt;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">●<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">      </span></span><span lang="EN">Representation of solution structures that enable generalization and transfer.</span></p><p class="MsoNormal" style="margin:0in;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN"> </span></p><div style="border-top:1pt none windowtext;border-bottom:1pt none windowtext;border-right-style:none;border-left-style:none;padding:10pt 0in"><p class="MsoNormal" style="margin:0in;line-height:normal;border:none;padding:0in;font-size:11pt;font-family:Arial,sans-serif"><b><span lang="EN">WORKSHOP FORMAT</span></b><span lang="EN"></span></p></div><div style="border-top:none;border-right:none;border-left:none;border-bottom:1pt none windowtext;padding:0in 0in 10pt"><p class="MsoNormal" style="margin:0in;text-align:justify;border:none;padding:0in;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">The workshop will feature multiple invited plenary and highlight talks as well as presentations of submitted papers. It will also include discussion sessions tuned to the topics presented at the workshop. The workshop is scheduled for<b> one day</b>. NeurIPS 2023 will be an in-person event this year, and the workshop will follow the same format as the conference.</span></p></div><div style="border-top:1pt none windowtext;border-bottom:1pt none windowtext;border-right-style:none;border-left-style:none;padding:10pt 0in"><p class="MsoNormal" style="margin:0in;text-align:justify;border-top:none;border-right:none;border-left:none;padding:0in 0in 10pt;border-bottom:0in none windowtext;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><b><span lang="EN">SUBMISSION REQUIREMENTS</span></b></p><p class="MsoNormal" style="margin:0in;text-align:justify;border:none;padding:10pt 0in 0in;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">Submissions can describe either work in progress or mature work that would be of interest to researchers working on generalization in planning. </span><span lang="EN" style="font-size:11.5pt;line-height:17.6333px;color:rgb(29,28,29);background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial">We also welcome “highlights” papers summarizing and highlighting results from multiple recent papers by the authors. Preference will be given to new work (including highlights) and work in progress rather than exact resubmissions of previously published work.</span><b><span lang="EN"></span></b></p></div><div style="border-top:none;border-right:none;border-left:none;border-bottom:1pt none windowtext;padding:0in 0in 10pt"><p class="MsoNormal" style="margin:0in;text-align:justify;border:none;padding:0in;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">Submissions of papers being reviewed at other venues (NeurIPS, CoRL, AAAI, ICRA, ICLR, AAMAS, CVPR, etc.) are welcome since GenPlan is a non-archival venue and we will not require a transfer of copyright. If such papers are currently under blind review, please anonymize the submission.</span></p></div><p class="MsoNormal" style="margin:0in 0in 10pt;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">Two types of papers can be submitted:</span></p><p class="MsoNormal" style="margin:0in 0in 0in 0.5in;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">●<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">      </span></span><span lang="EN">full technical papers with the length of up to 9 pages + references</span></p><p class="MsoNormal" style="margin:0in 0in 10pt 0.5in;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">●<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">      </span></span><span lang="EN">short papers with the length between 3 and 5 pages + references</span></p><div style="border-top:none;border-right:none;border-left:none;border-bottom:1pt none windowtext;padding:0in 0in 10pt;margin-left:0in;margin-right:4.5pt"><p class="MsoNormal" style="margin:10pt 0in 0in;border:none;padding:0in;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">Submissions should use the <a href="https://urldefense.com/v3/__https://neurips.cc/Conferences/2023/PaperInformation/StyleFiles__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpUT4JNlQ$" target="_blank">NeurIPS paper format</a>. The papers should adhere to the <a href="https://urldefense.com/v3/__https://nips.cc/public/CodeOfConduct__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpYUKHHHI$" target="_blank">NeurIPS Code of Conduct</a> and <a href="https://urldefense.com/v3/__https://neurips.cc/Conferences/2023/CallForPapers__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpJMW6vBw$" target="_blank">NeurIPS policy on using LLMs for writing</a> in their paper. Papers can be submitted via OpenReview at <a href="https://urldefense.com/v3/__https://openreview.net/group?id=NeurIPS.cc*2023*Workshop*GenPlan__;Ly8v!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpePtCG1A$" target="_blank">https://openreview.net/group?id=NeurIPS.cc/2023/Workshop/GenPlan</a></span></p></div><div style="border-right:none;border-bottom:none;border-left:none;border-top:1pt none windowtext;padding:10pt 0in 0in"><p class="MsoNormal" style="margin:0in 0in 10pt;line-height:normal;border:none;padding:0in;font-size:11pt;font-family:Arial,sans-serif"><b><span lang="EN">IMPORTANT DATES</span></b></p></div><p class="MsoNormal" style="margin:0in 0in 0in 47pt;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">●<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">      </span></span><span lang="EN">Paper submission deadline: <b>September 29, 2023 </b>(AoE, 11:59 PM UTC-12)</span></p><p class="MsoNormal" style="margin:0in 0in 0in 47pt;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">●<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">      </span></span><span lang="EN">Author notification: <b>October 20, 2023</b></span></p><p class="MsoNormal" style="margin:0in 0in 0in 47pt;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">●<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">      </span></span><span lang="EN">Camera-ready version due: <b>November 17, 2023</b></span></p><p class="MsoNormal" style="margin:0in 0in 0in 47pt;line-height:16.8667px;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN">●<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-kerning:auto;font-feature-settings:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">      </span></span><span lang="EN">Workshop date: <b>December (15-16, to be confirmed), 2023<br><br></b></span></p><div style="border-top:1pt none windowtext;border-bottom:1pt none windowtext;border-right-style:none;border-left-style:none;padding:10pt 0in"><p class="MsoNormal" style="margin:0in;line-height:normal;border:none;padding:0in;font-size:11pt;font-family:Arial,sans-serif"><b><span lang="EN">ORGANIZING COMMITTEE</span></b></p></div><div style="border-top:none;border-right:none;border-left:none;border-bottom:1pt none windowtext;padding:0in 0in 10pt"><p class="MsoNormal" style="margin:0in;line-height:22px;border:none;padding:0in;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN"><a href="https://urldefense.com/v3/__https://pulkitverma.net/__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpvNMSCv8$" target="_blank">Pulkit Verma</a>, Arizona State University, USA.</span></p><p class="MsoNormal" style="margin:0in;line-height:22px;border:none;padding:0in;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN"><a href="https://urldefense.com/v3/__http://siddharthsrivastava.net/__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOphlRtCBo$" target="_blank">Siddharth Srivastava</a>, Arizona State University, USA.</span></p><p class="MsoNormal" style="margin:0in;line-height:22px;border:none;padding:0in;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN"><a href="https://urldefense.com/v3/__https://avivt.github.io/avivt/__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpmNy_RPY$" target="_blank">Aviv Tamar</a>, Technion - Israel Institute for Technology, Israel.</span></p><p class="MsoNormal" style="margin:0in;line-height:22px;border:none;padding:0in;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN"><a href="https://urldefense.com/v3/__https://felipe.trevizan.org/__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpsy4nmdw$" target="_blank">Felipe Trevizan</a>, Australian National University, Australia.</span></p><p class="MsoNormal" style="margin:0in;line-height:20.0933px;border:none;padding:0in;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN"> </span></p><p class="MsoNormal" style="margin:0in;line-height:22px;border:none;padding:0in;font-size:11pt;font-family:Arial,sans-serif"><b><span lang="EN">ADVISORY BOARD<br></span></b><span lang="EN"><a href="https://urldefense.com/v3/__https://bonetblai.github.io/__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOp-GatZgs$" target="_blank">Blai Bonet</a>, Universitat Pompeu Fabra, Spain and Universidad Simón Bolívar, Venezuela</span></p><p class="MsoNormal" style="margin:0in;line-height:22px;border:none;padding:0in;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN"><a href="https://urldefense.com/v3/__https://www.diag.uniroma1.it/*degiacom/__;fg!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpzZWuwRc$" target="_blank">Giuseppe De Giacomo</a>, University of Oxford, United Kingdom<br><a href="https://urldefense.com/v3/__https://www-i6.informatik.rwth-aachen.de/*hector.geffner/__;fg!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOp52YYMZ8$" target="_blank">Hector Geffner</a>, RWTH Aachen University, Germany and Linköping University, Sweden<br><a href="https://urldefense.com/v3/__https://www.upf.edu/web/anders-jonsson__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpfThLR8I$" target="_blank">Anders Jonsson</a>, Universitat Pompeu Fabra, Spain<br><a href="https://urldefense.com/v3/__https://www.cs.toronto.edu/*sheila/__;fg!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpL_rex50$" target="_blank">Sheila McIlraith</a>, The University of Toronto, Canada</span></p><p class="MsoNormal" style="margin:0in;line-height:22px;border:none;padding:0in;font-size:11pt;font-family:Arial,sans-serif"><span lang="EN"><a href="https://urldefense.com/v3/__http://siddharthsrivastava.net/__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOphlRtCBo$" target="_blank">Siddharth Srivastava</a>, Arizona State University, USA<br><a href="https://urldefense.com/v3/__https://www.cs.utexas.edu/*pstone/__;fg!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpEJFQbzk$" target="_blank">Peter Stone</a>, The University of Texas at Austin, USA and Sony AI<br><a href="https://urldefense.com/v3/__https://users.cecs.anu.edu.au/*thiebaux/__;fg!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOp_oe_OVE$" target="_blank">Sylvie Thiébaux</a>, Australian National University, Australia and Université de Toulouse, France<br><a href="https://urldefense.com/v3/__https://groups.cs.umass.edu/shlomo/__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOprbmacoM$" target="_blank">Shlomo Zilberstein</a>, The University of Massachusetts Amherst, USA<br><br></span></p></div><span lang="EN" style="font-size:11pt;line-height:16.8667px;font-family:Arial,sans-serif">Please feel free to send workshop-related queries at <a href="mailto:genplan23.neurips@gmail.com" target="_blank">genplan23.neurips@gmail.com</a></span><br clear="all"><div><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div><br></div><div dir="ltr"></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>