DAI-23 features a special track "Call for Student Poster", providing students an opportunity to showcase and discuss their works (class projects, demonstrations, academic works, etc.) with members of related fields. The program warmly welcomes students at all levels: undergraduate, postgraduate, and Ph.D. Submissions can be based on your coursework, research works, or independent projects related to the theory and practice of distributed AI. Students are required to submit works for which they are the primary investigator, and all non-student collaborators (e.g., advisors) should be acknowledged appropriately (e.g., as co-authors or others). Note that DAI-23 will be an offline in-person conference, thus at least one author of each accepted paper is required to register and present the work in person at the conference. All accepted papers will not be published, thus authors are free to submit their works to any other venues.
Submitted papers must be no longer than 2 pages (a short paper) including references and in PDF format. All submissions must be anonymous, using the LaTeX or Word template below.
The submission must include the following: title; abstract; main body for technical description; any figures, tables, or diagrams; and references. An anonymous URL should be provided if supplementary materials (e.g., codes, vedio demos) are available. Submissions will be judged according to the criteria including clarity, relevance and contribution to the AI community, practical implications of the proposed ideas, etc.
Each accepted paper will be given a poster session at the conference. Authors of each accepted paper must prepare a poster to present their paper. At least one author must be available to discuss their work with visitors during their poster session at the conference.
Each author of accepted papers will be delivered a certificate. Authors can include the certificate in their academic portfolios, which can have a positive impact on their academic and professional trajectories, e.g., academic position applications (e.g., master or doctoral candidate applications), industry collaborations, by recognizing their contribution to the AI community.