[robocup-worldwide] [CfP] Deadline Extension to 25th of October, NIPS 2015 Workshop on Multi-Agent Systems

[Gerhard Neumann]

NIPS 2015 Workshop on Learning, Inference and Control of Multi-Agent Systems
12 December 2015, Montreal, Canada
https://malic15.wordpress.com/
Submission deadline: 25 October 2015

1. Call for Papers

Authors can submit a 2-6 pages paper (excluding references) that will be
reviewed by the organization committee. The papers can present new work or
give a summary of recent work of the author(s). All papers will be
considered for the poster sessions. Out-standing long papers (4-6 pages)
will also be considered for a 20 minutes oral presentation. Submissions
should be sent per email to malic.nips at gmail.com. Please use the standard
NIPS style-file for the submissions. Your submission should be anonymous,
so please do not add the author names to the PDF.

2. Workshop Overview

In the next few years, traditional single agent architectures will be more
and more replaced by actual multi-agent systems with components that have
increasing autonomy and computational power. This transformation has
already started with prominent examples such as power networks, where each
node is now an active energy generator, robotic swarms of unmaned aerial
vehicles, software agents that trade and negotiate on the Internet or robot
assistants that need to interact with other robots or humans. The number of
agents in these systems can range from a few complex agents up to several
hundred if not thousands of typically much simpler entities.

Multi-agent systems show many beneficial properties such as robustness,
scalability, paralellization and a larger number of tasks that can be
achieved in comparison to centralized, single agent architectures. However,
the use of multi-agent architectures represents a major paradigm shift for
systems design. In order to use such systems efficiently, effective
approaches for planning, learning, inference and communication are
required. The agents need to plan with their local view on the world and to
coordinate at multiple levels. They also need to reason about the
knowledge, observations and intentions of other agents, which can in turn
be cooperative or adversarial. Multi-agent learning algorithms need to deal
inherently with non-stationary environments and find valid policies for
interacting with the other agents.

Many of these requirements are inherently hard problems and computing their
optimal solutions is intractable. Yet, problems can become tractable again
by considering approximate solutions that can exploit certain properties of
a multi-agent system. Examples of such properties are sparse interactions
that only occur between locally neighbored agents or limited information to
make decisions (bounded rationality).

3. Goal

The fundamental challenges of this paradigm shift span many areas such as
machine learning, robotics, game theory and complex networks. This workshop
will serve as an inclusive forum for the discussion on ongoing or completed
work in both theoretical and practical issues related to the learning,
inference and control aspects of multi-agent systems

4. Format

The workshop will serve as a platform to bring researchers from the
different relevant communities together and foster discussions about the
next necessary developments for multi-agent systems. The workshop will
consists of five to six invited talks, a few contributed talks and a poster
session.

5. Confirmed Speakers

    Michael L. Littman (Brown University)
    Frans Oliehoek (University of Amsterdam)
    Christian Blum (University of the Basque Country)
    Michael Bowling (University of Alberta)
    Roderich Gross (University of Sheffield)
    Karl Tuyls (University of Liverpool)
    Vito Trianni (Italian National Research Council)

6. Topics

    Multi-Agent Reinforcement Learning
    POMDPs, Dec-POMDPS and Partially Observable Stochastic Games
    Multi-Agent Robotics, Human-Robot Collaboration, Swarm Robotics
    Game Theory, Algorithms for Computing Nash Equilibria and
    other Solution Concepts
    Swarm Intelligence
    Evolutionary Dynamics
    Complex Networks
    Mechanism Design
    Ad hoc teamwork

7. Workshop Organizers

    Vicenç Gómez (Universitat Pompeu Fabra)
    Gerhard Neumann (Technische Universität Darmstadt)
    Jonathan Yedidia (Disney Research)
    Peter Stone (University of Texas)

-- 
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Gerhard Neumann
Assistant Professor
TU Darmstadt, FB-Informatik
Fachgebiet Computational Learning for Autonomous Systems
Hochschulstr. 10, 64289 Darmstadt, Germany
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