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.Please feel free to send workshop related queries at: firstname.lastname@example.org.
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 sub-fields 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:
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:
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.
|Announcement and call for submissions||July 25, 2023|
|Paper submission deadline||September 29, 2023 (11:59 PM UTC-12)|
|Author notification||October 27, 2023|
|Camera ready version due||November 17, 2023|
|Workshop||December 16, 2023|