[robotics-worldwide] [meetings] Call for papers: Learning, Inference, and Control of Multi-Agent (and Multi-Robot) Systems

Amato, Chris c.amato at northeastern.edu
Thu Sep 21 07:28:11 PDT 2017


Title: Learning, Inference, and Control of Multi-Agent Systems (MALIC)
https://urldefense.proofpoint.com/v2/url?u=https-3A__sites.google.com_corp_view_malicaaai2018_&d=DwIFAg&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rStC00sfV_qVWqlcnmGhKrRoTMim5rLso-FTzVW2NfNyo&m=l6B5VQAgvTD6eucE7MKeikAkz82FcNTkdy3GLs2NO4s&s=u0dqw5QPbTkTFI5d0rGnTo8k4VNLPN7Rd4KhhODy4SI&e=

Description:

We live in a multi-agent world. To be successful in that world, intelligent agents need to learn to consider the agency of others. They will compete in marketplaces, cooperate in teams, communicate with others, coordinate their plans, and negotiate outcomes. Examples include self-driving cars interacting in traffic, personal assistants acting on behalf of humans and negotiating with other agents, swarms of unmanned aerial vehicles, financial trading systems, robotic teams, and household robots.

There has been great work on multi-agent learning in the past decade, but significant challenges remain, including the difficulty of learning an optimal model/policy from a partial signal, the exploration vs. exploitation dilemma, the scalability and effectiveness of learning algorithms, avoiding social dilemmas, learning emergent communication, learning to cooperate/compete in non-stationary environments with distributed simultaneously learning agents, and convergence guarantees.

We are interested in various forms of multi-agent learning for this symposium, including:
Learning in sequential settings in dynamic environments (such as stochastic games, decentralized POMDPs and their variants)
Learning with partial observability
Dynamics of multiple learners using (evolutionary) game theory
Learning with various communication limitations
Learning in ad-hoc teamwork scenarios
Scalability through swarms vs. intelligent agents
Bayesian nonparametric methods for multi-agent learning
Deep learning and reinforcement learning methods for multi-agent learning
Transfer learning in multi-agent settings
Applications of multi-agent learning

The purpose of this symposium is to bring together researchers from machine learning, control, neuroscience, robotics, and multi-agent communities with the goal of broadening the scope of multi-agent learning research and addressing the fundamental issues that hinder the applicability of multi-agent learning for complex real world problems. This symposium will present a mix of invited sessions, contributed talks and a poster session with leading experts and active researchers from relevant fields. Furthermore, the symposium is designed to allow plenty of time for discussions and initiating collaborations.

Authors can submit papers of 2-6 pages that will be reviewed by the organization committee. The papers can present new work or a summary of recent work. Submissions will be handled through easychair: https://urldefense.proofpoint.com/v2/url?u=https-3A__easychair.org_conferences_-3Fconf-3Dmalic18&d=DwIFAg&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rStC00sfV_qVWqlcnmGhKrRoTMim5rLso-FTzVW2NfNyo&m=l6B5VQAgvTD6eucE7MKeikAkz82FcNTkdy3GLs2NO4s&s=PHKyHe0ncJvXfE_xtXorWIO4kyiR1jpGuEoupw20tLw&e=

Organizing Committee:

Christopher Amato, Northeastern University
Thore Graepel, Google DeepMind
Joel Leibo, Google DeepMind
Frans Oliehoek, University of Liverpool
Karl Tuyls, Google DeepMind and University of Liverpool

Invited Speakers:

Sabine Hauert, University of Bristol, Bristol Robotics Lab, UK
Mykel Kochenderfer, Stanford University, US
Ann Nowe, Vrije Universiteit Brussel, Belgium
Peter Stone, University of Texas at Austin, US
Igor Mordatch, OpenAI, US
Nora Ayanian, USC, US









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