[agents] 2nd CfP: Adaptive and Learning Agents Workshop (ALA) at AAMAS 2024 (Auckland, New Zealand)

[Connor Yates]

** Apologies if you receive more than one copy. Please share with students
and colleagues. **

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 2024) in Auckland, New Zealand. Please find the
Call for Papers below.

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Adaptive and Learning Agents Workshop at AAMAS (Auckland, New Zealand)

Submission deadline: 5 February 2024

Extended versions of all original contributions at ALA 2024 will be
eligible for inclusion in a special issue of the Springer journal Neural
Computing and Applications
<https://www.springer.com/computer/ai/journal/521> (Impact Factor 5.606).

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TL;DR:

* Workshop with a long and successful history, now in its sixteenth 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.

* Submission Instructions: https://ala2024.github.io/

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IMPORTANT DATES:

* Submission Deadline:         February 5, 2024

* Notification of acceptance: March 4, 2024

* Camera-ready copies:   March 25, 2024

* Workshop: May 6 & 7, 2024

* Journal submission deadline: November 15, 2024

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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 optimise 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).

All aspects of adaptive and learning agents and multi-agent systems are on
topic for this workshop, but we will particularly encourage work that
modifies established learning techniques and/or creates 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 and learning (multi-agent) systems to model real
world complex systems

In addition to these topics, this year we are 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

Papers can be submitted through OpenReview, with instructions found on the
workshop website:  https://ala2024.github.io/

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 2024 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 2024 will
be eligible for inclusion in a special issue of the Springer journal Neural
Computing and Applications
<https://www.springer.com/computer/ai/journal/521> (Impact Factor 6.0).
Deadline for submitting extended papers: November 15, 2024.

We look forward to receiving your submissions,

- The Organizers

Raphael Avalos (Vrije Universiteit Brussel, BE)

Henrik Müller (Leibniz University Hannover, DE)

Caroline Wang (University of Texas at Austin, US)

Connor Yates (U.S. Naval Research Laboratory, US)

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