LICM 2015 - 2015 Workshop on Learning, Inference and Control of Multi-Agent Systems

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)