CFP: NeurIPS 2023 Workshop on Generalization in Planning
Pulkit Verma
GenPlan '23: NeurIPS 2023 Workshop on Generalization in
Planning
https://aair-lab.github.io/genplan23
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. NeurIPS 2023 will be held in New Orleans, December 10-16, 2023. Details below!
CONFIRMED INVITED SPEAKERS
Feryal Behbahani, Google DeepMind
Roberta Raileanu, Meta AI
Amy Zhang, The University of Texas at Austin, USA and Meta AI
Giuseppe De Giacomo, University of Oxford, United Kingdom
Hector Geffner, RWTH Aachen University, Germany and Linköping University, Sweden
Peter Stone, 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 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. The papers should adhere to the NeurIPS Code of Conduct and NeurIPS policy on using LLMs for writing in their paper. Papers can be submitted via OpenReview at https://openreview.net/group?id=NeurIPS.cc/2023/Workshop/GenPlan
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, Arizona State University, USA.
Siddharth Srivastava, Arizona State University, USA.
Aviv Tamar, Technion - Israel Institute for Technology, Israel.
Felipe Trevizan, Australian National University, Australia.
ADVISORY BOARD
Blai Bonet, Universitat Pompeu Fabra, Spain
and Universidad Simón Bolívar, Venezuela
Giuseppe De Giacomo, University of Oxford,
United Kingdom
Hector Geffner, RWTH Aachen University,
Germany and Linköping University, Sweden
Anders
Jonsson, Universitat Pompeu Fabra, Spain
Sheila
McIlraith, The University of Toronto, Canada
Siddharth
Srivastava, Arizona State University, USA
Peter
Stone, The University of Texas at Austin, USA and Sony AI
Sylvie
Thiébaux, Australian National University, Australia and Université
de Toulouse, France
Shlomo
Zilberstein, The University of Massachusetts Amherst, USA