Authors should submit full papers electronically in PDF format at OpenReview.net.
Formatting Guidelines: Please format papers according to the updated ICLR style file (Download).
Paper Length: Papers can be up to 8 pages long in ICLR format. Shorter submissions are appreciated, and we encourage authors to submit preliminary results and ideas. Additional pages may be used for references.
Supplemental material can be appended at the end of the paper. However, reviewers are instructed to make their evaluations based on the main submission, and are not obligated to consult the supplemental material.
Parallel Submissions: We encourage concurrent submission of papers submitted to our workshop to other workshops at ICLR 2022. To widen participation and encourage discussion, there will be no formal publication of workshop proceedings. We will, however, post the accepted papers online to the benefit of the participants to the workshop. Therefore, submission of preliminary work and papers to be submitted or in preparation for submission to other major venues in the field are encouraged.
Past Submissions: We will not accept direct submissions of previously published work, however, we expect surveys of collections of previously published works that are less well known to the ICLR community to be of value to workshop attendees. We will consider any submission that aims to present a synopsis of previous research both to prevent “reinventing the wheel” and to re-inspire future extensions of classic approaches. We will prioritize surveys of work prior to the current era of modern AI (e.g., pre-2011). Accepted papers to ICLR2022 are not considered as past published works and are eligible submissions for this workshop as they have not been exposed for a long time to the community.
We invite papers on the wide range of topics that fit within the mission of the workshop, which is a dynamical system / multiagent view to machine learning algorithms. Therefore, the submission can be originated from each of the following topics but certainly not restricted to this set. Please find a list of example papers at the end of this page.
Multiagent Reinforcement Learning
Learning in Games (e.g., solution concepts and equilibria)
Distributed computation (e.g. in distributed systems, or neural computation)
Cyber-physical and other human-in-the-loop formulations
Questions and Discussions: Please join the following Slack workspace in case you have any question regarding the workshop and the submission process:https://join.slack.com/t/gamificationmas/shared_invite/zt-10ekqbjyp-A40B1RKwQsLtlXldbpD7Iw
At least one author from each submission is expected to serve as a reviewer.
The Cooperative AI Foundation awards 2 x $500 to two accepted papers.