Dear all,
We are organizing the next iteration of the Adaptive and Learning Agents (ALA) workshop at the International Conference on Autonomous Agents and Multiagent Systems (AAMAS) in London (Virtual). Please find the CfP below.
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Adaptive and Learning Agents Workshop at AAMAS (London, UK – Virtual)
Submission deadline: February 10, 2021
Extended versions of all original contributions at ALA 2021 will be eligible for inclusion in a special issue of the Springer journal Neural Computing and Applications (Impact Factor 4.774).
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TL;DR:
* Workshop with a long and successful history, now in its thirteenth edition.
* Covering all aspects of adaptive and learning agents and multi-agent systems research.
* Open to original research papers, work-in-progress, and visionary outlook papers, as well as presentations on recently published journal papers.
* ACM proceedings (AAMAS) format up to 8 pages (excluding references) for original research, up to 6 pages for work-in-progress and outlook papers (shorter papers are also welcome and will not be judged differently) and 2 pages for recently published journal papers.
* Accepted papers are eligible for inclusion in a post-proceedings journal special issue.
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IMPORTANT DATES:
* Submission Deadline: February 10, 2021
* Notification of acceptance: March 10, 2021
* Camera-ready copies: March 24, 2021
* Workshop: May 3 & 4, 2021
* Extended submission deadline: September 15, 2021
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OVERVIEW
Adaptive and learning agents, particularly those interacting with each other in a multi-agent setting, are becoming increasingly prominent as the size and complexity of real-world systems grows. How to adaptively control, coordinate and optimize such systems is an emerging multi-disciplinary research area at the intersection of Computer Science, Control Theory, Economics, and Biology. The ALA workshop will focus on agents and multi-agent systems which employ learning or adaptation.
The goal of this workshop is to increase awareness of and interest in adaptive agent research, encourage collaboration and give a representative overview of current research in the area of adaptive and learning agents and multi-agent systems. It aims at bringing together not only scientists from different areas of computer science but also from different fields studying similar concepts (e.g., game theory, bio-inspired control, mechanism design).
This workshop will focus on all aspects of adaptive and learning agents and multi-agent systems with a particular emphasis on how to modify established learning techniques and/or create new learning paradigms to address the many challenges presented by complex real-world problems.
The topics of interest include but are not limited to:
* Novel combinations of reinforcement and supervised learning approaches
* Integrated learning approaches using reasoning modules like negotiation, trust, coordination, etc.
* Supervised and semi-supervised multi-agent learning
* Reinforcement learning in multi-agent systems
* Novel deep learning approaches for adaptive single and multi-agents systems
* Human-in-the-loop learning systems
* Planning and Reasoning (single and multi-agent)
* Distributed learning
* Adaptation and learning in dynamic environments
* Evolution and Co-evolution of agents in complex multi-agent environments
* Cooperative exploration
* Learning to cooperate and collaborate
* Learning trust and reputation
* Communication restrictions and their impact on multi-agent coordination
* Design of reward structure and fitness measures for coordination
* Scaling learning techniques to large systems of agents
* Emergent behavior in adaptive multi-agent systems
* Game theoretical analysis of adaptive multi-agent systems
* Neuro-control for adaptation in multi-agent systems
* Bio-inspired multi-agent systems
* Adaptive and learning agents for multi-objective decision making
* Multiple objectives in (multi-)agent systems
* Applications of adaptive agents, learning agents, and multi-agent systems to real world complex systems
In addition to these topics, this year we are particularly interested in exploring negative results that can serve as guidelines for early-stage researchers in the field of adaptive and learning single/multi-agent systems.
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SUBMISSION DETAILS
We invite submission of original work, up to 8 pages in length (excluding references) in the ACM proceedings format (i.e. following the AAMAS formatting instructions). This includes work that has been accepted as a poster/extended abstract at the AAMAS 2021 conference. Additionally, we welcome submission of preliminary results, i.e. work-in-progress, as well as visionary outlook papers that lay out directions for future research in a specific area, both up to 6 pages in length, although shorter papers are very much welcome, and will not be judged differently. Finally, we also accept recently published journal papers in the form of a 2 page abstract.
All submissions will be peer-reviewed (single-blind). Accepted work will be allocated time for poster and possibly oral presentation during the workshop. Extended versions of all original contributions at ALA 2021 will be eligible for inclusion in a special issue of the Springer journal Neural Computing and Applications (Impact Factor 4.774). Deadline for submitting extended papers: September 15, 2021.
We look forward to receiving your submissions,
– The Organizers
Conor F. Hayes (NUI Galway, IE)
Roxana Rădulescu (Vrije Universiteit Brussel, BE)
Diederik M. Roijers (Vrije Universiteit Brussel, BE & HU University of Applied Sciences Utrecht, NL)
Fernando P. Santos (Princeton University, USA)
Felipe Leno da Silva (University of São Paulo, BR)
Collective Intelligence, co-published by ACM and SAGE, with the collaboration of Nesta, is a global, peer-reviewed, open-access journal devoted to advancing the theoretical and empirical understanding of collective performance in diverse systems. These systems can include human organizations, hybrid AI-human teams, computer networks, adaptive matter, cellular systems, neural circuits, animal societies, nanobot swarms, and others. The journal embraces a policy of creative rigor in the study of collective intelligence to facilitate the discovery of principles that apply across scales and new ways of harnessing the collective to improve social, ecological, and economic outcomes. In that spirit, the journal encourages a broad-minded approach to collective performance. It welcomes perspectives that emphasize traditional views of intelligence as well as optimality, satisficing, robustness, adaptability, and wisdom.
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