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** Apologies if you receive more than one copy. Please share with students and colleagues. ** </div>
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Dear all, </div>
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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 Montreal. Please find the CfP below. </div>
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Adaptive and Learning Agents Workshop at AAMAS ( Montreal, Canada ) </div>
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<a href="http://ala2019.vub.ac.be/" class="">http://ala2019.vub.ac.be/</a> </div>
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Submission deadline: February 12, 2019 </div>
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TL;DR:</div>
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* Workshop with a long and successful history, now in its eleventh edition.</div>
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* Covering all aspects of adaptive and learning agents and multi-agent systems research.</div>
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* Open to original research papers, work-in-progress, and visionary outlook papers, as well as presentations on recently published journal papers.</div>
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* 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.</div>
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* Accepted papers are eligible for inclusion in a post-proceedings journal special issue.</div>
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* Submissions through easychair: <a href="https://easychair.org/conferences/?conf=ala2019" class="">
https://easychair.org/conferences/?conf=ala2019</a> </div>
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IMPORTANT DATES: </div>
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* Submission Deadline:<span class="Apple-tab-span" style="margin: 0px; padding: 0px; border: 0px; white-space: pre;">
</span>February 12, 2019 </div>
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* Notification of acceptance:<span class="Apple-tab-span" style="margin: 0px; padding: 0px; border: 0px; white-space: pre;">
</span>March 10, 2019</div>
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* Camera-ready copies:<span class="Apple-tab-span" style="margin: 0px; padding: 0px; border: 0px; white-space: pre;">
</span>March 24, 2019</div>
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* Workshop: <span class="Apple-tab-span" style="margin: 0px; padding: 0px; border: 0px; white-space: pre;">
</span>May 13 & 14, 2019</div>
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OVERVIEW </div>
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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. </div>
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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). </div>
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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: </div>
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* Novel combinations of reinforcement and supervised learning approaches </div>
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* Integrated learning approaches that work with other agent reasoning modules like negotiation, trust models, coordination, etc. </div>
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* Supervised multi-agent learning</div>
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* Reinforcement learning (single and multi-agent)</div>
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* Novel deep learning approaches for adaptive single and multi-agent systems </div>
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* Multi-objective optimisation in single- and multi-agent systems</div>
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* Planning (single and multi-agent)</div>
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* Reasoning (single and multi-agent)</div>
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* Distributed learning</div>
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* Adaptation and learning in dynamic environments</div>
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* Evolution of agents in complex environments</div>
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* Co-evolution of agents in a multi-agent setting</div>
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* Cooperative exploration and learning to cooperate and collaborate</div>
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* Learning trust and reputation</div>
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* Communication restrictions and their impact on multi-agent coordination </div>
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* Design of reward structure and fitness measures for coordination</div>
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* Scaling learning techniques to large systems of learning and adaptive agents</div>
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* Emergent behavior in adaptive multi-agent systems</div>
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* Game theoretical analysis of adaptive multi-agent systems</div>
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* Neuro-control in multi-agent systems</div>
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* Bio-inspired multi-agent systems</div>
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* Adaptive and learning agents for multi-objective decision making</div>
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* Applications of adaptive and learning agents and multi-agent systems to real world complex </div>
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systems </div>
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SUBMISSION DETAILS </div>
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Papers can be submitted through EasyChair: <a href="https://easychair.org/conferences/?conf=ala2019" class="">
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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 2019
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. </div>
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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 original papers presented at the workshop will also be eligible for inclusion in a
post-proceedings journal special issue (journal to be confirmed).</div>
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We look forward to your submissions, </div>
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- The Organizers </div>
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Patrick MacAlpine (Microsoft Research, USA)</div>
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Patrick Mannion (Galway-Mayo Institute of Technology, IE) </div>
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Bei Peng (Microsoft Research, USA)</div>
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Roxana Rădulescu (Vrije Universiteit Brussel, BE) </div>
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