AAMAS-20: The Free Conference on Autonomous Agents and Multi-Agent Systems
Multi-agent systems are increasingly important. Here’s a free resource to learn about cutting edge research!
Agents, entities that can interact with their environment or other agents, are an increasingly important field of artificial intelligence. Agents can learn, reason about others, adopt norms, and interact with humans in both virtual and physical settings. This field includes contributions to many areas across artificial intelligence, including game theory, machine learning, robotics, human-agent interaction, modeling, and social choice. While the majority of the work is academic investigations with lab-based experiments, there have also been a growing number of real-world deployments with direct impact on governmental and commercial organizations. The international AAMAS conference, now in its 19th year, is taking place online, and attendance is free, while registration typically costs over $700 USD.
This conference begins on Saturday May 9, with two days of workshops and tutorials. Monday May 11-Wednesday May 13 has over 300 paper presentations and keynote presentations by luminaries such as Carla Gomes, Alison Heppenstall, Sergey Levine, and Thore Graepel. The ACM SIGAI Autonomous Agents Research Award talk will be delivered by Muninder P. Singh, and Dominik Peters will give the Victor Lesser Dissertation Award presentation.
While all videos will be available on underline.io through the end of 2020 for asynchronous viewing, underline.io will also support live Q&A sessions during the event. Proceedings of the conference will be available online, free of charge, as always.
If you are interested in learning more about the exciting area of autonomous agents and multi-agent systems, we hope you will join us!
— Matthew Taylor, on behalf of Amal El Fallah Seghrouchni and Gita Sukthankar, AAMAS’20 General Chairs
Additional Details
The Autonomous Agents and Multiagent Systems (AAMAS) conference series gathers researchers from around the world to share the latest advances in the field. It is the premier forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS 2002, the first of the series, was held in Bologna, followed by Melbourne (2003), New York (2004), Utrecht (2005), Hakodate (2006), Honolulu (2007), Estoril (2008), Budapest (2009), Toronto (2010), Taipei (2011), Valencia (2012), Saint Paul (2013), Paris (2014), Istanbul (2015), Singapore (2016), São Paulo (2017), Stockholm (2018) and Montréal (2019). This volume is the proceedings of AAMAS 2020, the 19th conference in the series, which was to be held in Auckland in May 2020.
AAMAS 2020 invited submissions for a general track, a Blue Sky Ideas track, and a track to present papers from JAAMAS (the journal Autonomous Agents and Multi-Agent Systems) that had not previously been presented at a major conference. The Blue Sky Ideas track was chaired by Alessandro Ricci and Juan Antonio Rodriguez. Rym Zalila-Wenkstern and Pınar Yolum solicited papers for the JAAMAS Presentation Track from the papers that appeared in JAAMAS within the preceding 12 months.
A group of Area Chairs (AC) was selected to help oversee the review process of the main track. The ACs performed an initial check of submissions and recommended summary rejection of those that did not meet the AAMAS scope, submission or formatting instructions.
Jointly with the program chairs, the chairs of the ten areas were responsible for appointing Senior Program Committee (SPC) members, who in turn helped identify a strong and diverse set of Program Committee (PC) members. PC could review for more than one area. Every paper was reviewed by at least three PC members, overseen by an SPC member who ensured reviews were clear and informative. After authors were given an opportunity to respond to the reviewers, the SPC member led a discussion where the reviewers considered each others’, and the authors’, comments. The area chairs in turn worked with the program chairs to make final decisions about acceptance for the papers, to ensure uniformly high quality.
AAMAS 2020 attracted a good number of high-quality submissions: the overall acceptance rate for full papers was 23% (186 out of 808 reviewed submissions were accepted).
While all the accepted papers are of high quality, a selected few papers from the main track were nominated for the Best Paper Award and the Pragnesh Jay Modi Best Student Paper Award. The Best Paper Award was presented at the conference to the best paper, and the Pragnesh Jay Modi Best Student Paper Award was given to the best of the remaining papers primarily authored by a student. The Best Student Paper Award was sponsored by Springer. The nominees for these awards are listed below, alphabetically by the first author’s last name; papers primarily authored by a student are marked with an asterisk (*). These papers were also nominated for expedited review at the Journal of Artificial Intelligence Research (JAIR).
- Harshavardhan Kamarthi, Priyesh Vijayan, Bryan Wilder, Balaraman Ravindran, and Milind Tambe. Influence Maximization in Unknown Social Networks: Learning Policies for Effective Graph Sampling
- Divya Ramesh, Anthony Z. Liu, Andres J. Echeverria, Jean Y. Song, Nicholas R. Waytowich, and Walter S. Lasecki. Yesterday’s Reward is Today’s Punishment: Contrast Effects in Human Feedback to Reinforcement Learning Agents
- Klaus Weber, Kathrin Janowski, Niklas Rach, Katharina Weitz, Wolfang Minker, Stefan Ultes, and Elisabeth André. Predicting Persuasive Effectiveness for Multimodal Behavior Adaptation using Bipolar Weighted Argument Graphs
- Shangtong Zhang, Wendelin Boehmer, and Shimon Whiteson. Deep Residual Reinforcement Learning
In addition, the IFAAMAS Influential Paper award will be presented at the conference for the following two papers:
- Ariel D. Procaccia and Moshe Tennenholtz. Approximate Mechanism Design without Money. In Proceedings of the 10th ACM Conference on Electronic Commerce, pp. 177–186, 2009.
- Kurt M. Dresner and Peter Stone. A Multiagent Approach to Autonomous Intersection Management. Journal of Artificial Intelligence Research, vol 31, pp. 591–656, 2008.
