[agents] [1st CFP] NIPS 2015 Workshop on Learning, Inference and Control of Multi-Agent Systems

[Vicenç Gómez]

NIPS 2015 Workshop on Learning, Inference and Control of Multi-Agent 
Systems
12 December 2015, Montreal, Canada
https://malic15.wordpress.com/
Submission deadline: 11 October 2015

1. Call for Papers

Authors can submit a 2-6 pages paper 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. Invited Speakers

     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)