AAAI 2025 Workshop on Generalization in Planning
Rashmeet Kaur Nayyar
[Apologies for cross-posting]
GenPlan’25: AAAI 2025 Workshop on Generalization in Planning
Workshop Website: https://aair-lab.github.io/genplan25
TL; DR: This AAAI workshop will feature recent research on methods and representations for generalization and transfer in all forms of planning and learning for planning. Please consider submitting your recent work as well as surveys highlighting your recent results by November 22, 2024 at https://bit.ly/SubmitToGenPlan25. AAAI 2025 will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25th to March 4th. Details below!
Invited Speakers
Michael Littman, Brown University
Sheila Mcllraith, University of Toronto
Daniele Meli, University of Verona
(two more speakers to be confirmed soon!)
Workshop Overview
Finding solutions to sequential decision-making (SDM) problems that generalize across problem instances and domains is crucial to the advancement of artificial intelligence (AI). Generalized solutions broaden access to AI algorithms, reduce resource consumption, and enable knowledge discovery at a broad scale. Recent advances in deep reinforcement learning and generative AI have led to data-driven methods that are effective for short-horizon reasoning and decision-making, with open problems regarding sample efficiency, guarantees of correctness, and applicability to long-horizon settings. On the other hand, the AI planning community has made complementary strides, developing robust analytical methods that enable sample-efficient generalization and transferability in long-horizon planning, with open problems in designing and modeling representations. This workshop aims to unify relevant research that is often fragmented across separate research communities, including AI planning, deep learning, reinforcement learning, logic programming, model learning, and robotics.
We welcome submissions addressing the problem of generalizable and transferable learning in all forms of sequential decision making. This event represents the eighth 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. The exact date would be announced soon.
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 “highlight” 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.
Two types of papers can be submitted:
● full technical papers with the length of up to 7 pages + references
● short papers with the length between 3 and 5 pages + references
Please format submissions in AAAI style. Some accepted long papers will be selected for contributed talks, and all accepted papers will be presented as posters. Papers under review at other venues are welcome since GenPlan is non-archival and does not require copyright transfer. If such papers are currently under blind review, please anonymize the submission. Accepted papers will be made available digitally on the workshop's website.
Submissions are accepted through Openreview.
IMPORTANT DATES
● Paper submission deadline: November 22, 2024 (AoE, 11:59 PM UTC-12)
● Author notification: December 9, 2024
● Camera-ready version due: TBA
● Workshop date: March 3 or March 4, 2025
ORGANIZING COMMITTEE
Rashmeet Kaur Nayyar, Arizona State University
Naman Shah, Brown University
Forest Agostinelli, University of South Carolina
Abhinav Bhatia, University of Massachusetts Amherst
Misagh Soltani, University of South Carolina
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
Please feel free to send workshop-related queries at genpla...@gmail.com