[PlanetKR] Call for Papers: NeurIPS 2023 Workshop on Generalization in Planning

Thu Jul 27 05:24:09 UTC 2023

[Apologies for cross-posting]

*GenPlan '23: NeurIPS 2023 Workshop on Generalization in Planning*
https://aair-lab.github.io/genplan23
<https://urldefense.com/v3/__https://aair-lab.github.io/genplan23__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpwAScV1g$>

*TL; DR:* 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
https://bit.ly/SubmitToGenPlan23
<https://urldefense.com/v3/__https://bit.ly/SubmitToGenPlan23__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpywrMdXI$>
. NeurIPS 2023
<https://urldefense.com/v3/__https://neurips.cc/Conferences/2023__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpq6mwgNE$>
will
be held in New Orleans, December 10-16, 2023. Details below!

*CONFIRMED INVITED SPEAKERS*

Feryal Behbahani
<https://urldefense.com/v3/__https://feryal.github.io/__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpuJc4ljg$>,
Google DeepMind

Roberta Raileanu
<https://urldefense.com/v3/__https://rraileanu.github.io/__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpLt-eGWU$>,
Meta AI

Amy Zhang
<https://urldefense.com/v3/__https://amyzhang.github.io/__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOp8xfhGiQ$>,
The University of Texas at Austin, USA and Meta AI

Giuseppe De Giacomo
<https://urldefense.com/v3/__https://www.diag.uniroma1.it/*degiacom/__;fg!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpzZWuwRc$>,
University of Oxford, United Kingdom

Hector Geffner
<https://urldefense.com/v3/__https://www-i6.informatik.rwth-aachen.de/*hector.geffner/__;fg!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOp52YYMZ8$>,
RWTH Aachen University, Germany and Linköping University, Sweden

Peter Stone
<https://urldefense.com/v3/__https://www.cs.utexas.edu/*pstone/__;fg!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpEJFQbzk$>,
The University of Texas at Austin, USA and Sony AI

*WORKSHOP OVERVIEW*

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.

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.

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 GenPlan
<https://urldefense.com/v3/__http://www.genplan.ai/__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOp6eeVvyE$>
series
of Workshops.

*TOPICS*

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 *formal* as well as *empirical* issues on
topics such as:

●      Formulations of generalized SDM problems.

●      Representations, learning and synthesis for generalized plans and
policies.

●      Learning for transfer and generalization in reinforcement learning.

●      Learning and representing hierarchical policies and behaviors for
SDM.

●      Learning and synthesis of generalizable solutions for SDM problem
classes.

●      Learning paradigms, representations, and algorithms for transferring
learned knowledge and solutions to new SDM problems.

●      Learning and representing generalized Q/V functions and heuristics
for plan and policy generalization.

●      Learning high-level models and hierarchical solutions for
generalizable SDM.

●      Neuro-symbolic approaches for generalization and transfer in SDM.

●      Few-shot learning and transfer for SDM.

●      Meta-learning for generalizable policies.

●      Learning for program synthesis.

●      Learning domain control knowledge and partial policies.

●      Generalization and transfer in robot planning problems.

●      Representation of solution structures that enable generalization and
transfer.

*WORKSHOP FORMAT*

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* one day*. NeurIPS 2023 will be an in-person event this year,
and the workshop will follow the same format as the conference.

*SUBMISSION REQUIREMENTS*

Submissions can describe either work in progress or mature work that would
be of interest to researchers working on generalization in planning. 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.

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.

Two types of papers can be submitted:

●      full technical papers with the length of up to 9 pages + references

●      short papers with the length between 3 and 5 pages + references

Submissions should use the NeurIPS paper format
<https://urldefense.com/v3/__https://neurips.cc/Conferences/2023/PaperInformation/StyleFiles__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpUT4JNlQ$>.
The papers should adhere to the NeurIPS Code of Conduct
<https://urldefense.com/v3/__https://nips.cc/public/CodeOfConduct__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpYUKHHHI$>
 and NeurIPS policy on using LLMs for writing
<https://urldefense.com/v3/__https://neurips.cc/Conferences/2023/CallForPapers__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpJMW6vBw$>
in
their paper. Papers can be submitted via OpenReview at
https://openreview.net/group?id=NeurIPS.cc/2023/Workshop/GenPlan
<https://urldefense.com/v3/__https://openreview.net/group?id=NeurIPS.cc*2023*Workshop*GenPlan__;Ly8v!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpePtCG1A$>

*IMPORTANT DATES*

●      Paper submission deadline: *September 29, 2023 *(AoE, 11:59 PM
UTC-12)

●      Author notification: *October 20, 2023*

●      Camera-ready version due: *November 17, 2023*

●      Workshop date:

*December (15-16, to be confirmed), 2023*

*ORGANIZING COMMITTEE*

Pulkit Verma
<https://urldefense.com/v3/__https://pulkitverma.net/__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpvNMSCv8$>,
Arizona State University, USA.

Siddharth Srivastava
<https://urldefense.com/v3/__http://siddharthsrivastava.net/__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOphlRtCBo$>,
Arizona State University, USA.

Aviv Tamar
<https://urldefense.com/v3/__https://avivt.github.io/avivt/__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpmNy_RPY$>,
Technion - Israel Institute for Technology, Israel.

Felipe Trevizan
<https://urldefense.com/v3/__https://felipe.trevizan.org/__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpsy4nmdw$>,
Australian National University, Australia.

*ADVISORY BOARD*Blai Bonet
<https://urldefense.com/v3/__https://bonetblai.github.io/__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOp-GatZgs$>,
Universitat Pompeu Fabra, Spain and Universidad Simón Bolívar, Venezuela

Giuseppe De Giacomo
<https://urldefense.com/v3/__https://www.diag.uniroma1.it/*degiacom/__;fg!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpzZWuwRc$>,
University of Oxford, United Kingdom
Hector Geffner
<https://urldefense.com/v3/__https://www-i6.informatik.rwth-aachen.de/*hector.geffner/__;fg!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOp52YYMZ8$>,
RWTH Aachen University, Germany and Linköping University, Sweden
Anders Jonsson
<https://urldefense.com/v3/__https://www.upf.edu/web/anders-jonsson__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpfThLR8I$>,
Universitat Pompeu Fabra, Spain
Sheila McIlraith
<https://urldefense.com/v3/__https://www.cs.toronto.edu/*sheila/__;fg!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpL_rex50$>,
The University of Toronto, Canada

Siddharth Srivastava
<https://urldefense.com/v3/__http://siddharthsrivastava.net/__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOphlRtCBo$>,
Arizona State University, USA
Peter Stone
<https://urldefense.com/v3/__https://www.cs.utexas.edu/*pstone/__;fg!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOpEJFQbzk$>,
The University of Texas at Austin, USA and Sony AI
Sylvie Thiébaux
<https://urldefense.com/v3/__https://users.cecs.anu.edu.au/*thiebaux/__;fg!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOp_oe_OVE$>,
Australian National University, Australia and Université de Toulouse, France
Shlomo Zilberstein
<https://urldefense.com/v3/__https://groups.cs.umass.edu/shlomo/__;!!IKRxdwAv5BmarQ!akDB7JfAwApf5QbyVw24xxZZ7LrGRK73O5LJ9XhakCHz5XIvglGhBnaOnBGu3J799lpvI6u0tKOprbmacoM$>,
The University of Massachusetts Amherst, USA

Please feel free to send workshop-related queries at
genplan23.neurips at gmail.com
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