The submissions deadline for RLDM2022 was 15 February 2022. Authors were notified of acceptances on 15 March 2022.
All abstracts of accepted submissions will be available in the RLDM 2022 Abstracts Book. In addition, if you wish, your up-to-4-page extended abstract can be included in the RLDM 2022 Extended Abstracts Book.
See POSTER INFORMATION for guidelines about poster formatting and style.
Inclusion of your extended abstract is OPTIONAL. If you want your extended abstract to appear in the electronic proceedings, you must upload a camera-ready version to CMT (https://cmt3.research.microsoft.com/RLDM2022) by May 15th. You can also modify your abstract, title or author list as part of this process.
If you decide to upload a camera-ready extended abstract, we ask that you follow the suggestions made by the reviewers of your abstract.
PREVIOUS CALL FOR ABSTRACTS
We invite extended abstracts for contributed poster presentations and oral presentations.
We welcome submissions of original research related to “learning and decision making over time to achieve a goal”, coming from any discipline or disciplines, describing empirical results from human, animal or animat experiments, and/or theoretical work, simulations and modeling. Contributions should be aimed at an interdisciplinary audience, but not at the expense of technical excellence. This is an abstract-based meeting, with no published conference proceedings. As such, work that is intended for, or has been submitted to, other conferences or journals is also welcome, provided that the intent of communication to other disciplines is clear.
Submissions should consist of a summary (max 2000 characters; text only), and an extended abstract of between one and four pages (including figures and references). LaTeX and RTF templates, and sample submissions, are available here: https://rldm.org/submit/
Note: Only the summary will be made available in the (electronic) abstract booklets. The extended abstract will be used for reviewing, and will be available online only pending on authors’ separate explicit permission. Online availability will have no bearing on the review process and authors are encouraged to include new, unpublished, findings which they do not want to make publicly available.
To submit your abstract please go to https://cmt3.research.microsoft.com/RLDM2022
Submissions will be reviewed for relevance to the topic and for quality. Exceptional abstracts will be selected for oral presentations and for poster spotlight presentations.