[robotics-worldwide] [meetings] First CfP: Adaptive and Learning Agents Workshop at Federated AI Meeting 2018 (Stockholm, Sweden)

Kaushik Subramanian kau.subbu at gmail.com
Sun Mar 11 23:32:06 PDT 2018

*Adaptive and Learning Agents Workshop at Federated AI Meeting 2018
(Stockholm, Sweden)** 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
Federated AI Meeting (FAIM) in Stockholm. Please find the CfP
below.*******************************************************Adaptive and
Learning Agents Workshop at Federated AI Meeting (Stockholm,
<https://urldefense.proofpoint.com/v2/url?u=http-3A__ala2017.it.nuigalway.ie_&d=DwIBaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=Wvk9LDAos0kDVuS2p5-MHMiYDCRRcbjRrzysh0EzXps&s=KnPuDBec-AD9Y2CQSqNdkj-2gmiDVwbsuymkvjUuJzM&e=>Submission deadline: APRIL 22,
2018*******************************************************TL;DR:* Workshop
with a long and successful history, now in its eleventh 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 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:
DATES:* Submission Deadline:         April 22, 2018* Notification of
acceptance: May 21, 2018* Camera-ready copies:         June 4, 2018*
Workshop: July 14/15,
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 agent and
multi-agent systems which employ learning or adaptation.The goal of this
workshop is to increase awareness 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 that work with other agent reasoning modules like negotiation,
trust models, coordination, etc.   * Supervised multi-agent learning   *
Reinforcement learning (single and multi-agent)   * Novel deep learning
approaches for adaptive single and multi-agent systems   * Multi-objective
optimisation in single- and multi-agent systems   * Planning (single and
multi-agent)   * Reasoning (single and multi-agent)   * Distributed
learning   * Adaptation and learning in dynamic environments   * Evolution
of agents in complex environments   * Co-evolution of agents in a
multi-agent setting   * Cooperative exploration and 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 learning and adaptive agents   * Emergent
behavior in adaptive multi-agent systems   * Game theoretical analysis of
adaptive multi-agent systems   * Neuro-control in multi-agent systems   *
Bio-inspired multi-agent systems   * Adaptive and learning agents for
multi-objective decision making   * Applications of adaptive and learning
agents and multi-agent systems to real world complex
DETAILSPapers can be submitted through EasyChair:
<https://urldefense.proofpoint.com/v2/url?u=https-3A__easychair.org_conferences_-3Fconf-3Dala-5F2018&d=DwIBaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=Wvk9LDAos0kDVuS2p5-MHMiYDCRRcbjRrzysh0EzXps&s=S-4rmAjkpS-T08rbFfvFe4Ym8ax1CPPluhZ56PXJM6s&e=> 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 any
of the FAIM 2018 conferences. 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 original papers presented at the workshop will also be eligible
for inclusion in a post-proceedings special issue of The Knowledge
Engineering Review journal (Impact Factor 1.510).We look forward to your
submissions,- The OrganizersAnna Harutyunyan (DeepMind, UK)Patrick Mannion
(Galway-Mayo Institute of Technology, IE)Bei Peng (Washington State
University, USA)Kaushik Subramanian (Georgia Institute of Technology,

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