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ALA 2021 : Adaptive and Learning Agents @ AAMAS 2021Conference Series : Adaptive and Learning Agents | |||||||||||||||
Link: https://ala2021.vub.ac.be/ | |||||||||||||||
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Call For Papers | |||||||||||||||
1st CfP: Adaptive and Learning Agents Workshop at AAMAS 2021 (London, UK)
******************************************************* Adaptive and Learning Agents Workshop at AAMAS (London, UK) https://ala2021.vub.ac.be 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). ******************************************************* 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. * Submissions through easychair: https://easychair.org/conferences/?conf=ala2021 ******************************************************* IMPORTANT DATES: * Submission Deadline: February 10, 2021 * Notification of acceptance: March 10, 2021 * Camera-ready copies: March 24, 2021 * Workshop: May 3 & 4, 2020 * Extended submission deadline: September 15, 2021 ******************************************************* 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. ******************************************************* SUBMISSION DETAILS Papers can be submitted through EasyChair: https://easychair.org/conferences/?conf=ala2021 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) ================================================================== |
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