[agents] CfP: AAAI Spring Symposium on Multiagent Learning for the Real World

[Christopher Amato]

***** Challenges and Opportunities in Multiagent Learning for the Real World ****
AAAI Spring Symposium
March 21–23, 2016
Palo Alto, CA
http://miaoliu.scripts.mit.edu/SSS-16 <http://miaoliu.scripts.mit.edu/SSS-16>

Submission deadline: October 9, 2015

Developing efficient methods for multiagent learning has been a long-standing research focus in the Artificial intelligence, Game theory, Control, and Neuroscience communities.  As a growing number of agents are deployed in complex environments for scientific research and human well-being, there are increasing demands to design efficient learning algorithms that can be used in these real-world settings (including accounting for uncertainty, partial observability, sequential settings and communication restrictions). These challenges exist in many domains, such as underwater exploration, planetary navigation, robot soccer, stock-trading systems, and e-commerce.

Multiagent learning has had many successes, but significant challenges remain. For this symposium, we are interested in improving methods and integrating techniques from different research areas. Topics of interest include:

	• Learning in sequential settings in dynamic environments (such as stochastic games, decentralized POMDPs and their variants)
	• Learning with partial observability
	• Learning with various communication limitations
	• Learning in ad-hoc teamwork scenarios
	• Scalability through swarms vs. intelligent agents
	• Bayesian nonparametric methods for multiagent learning
	• Deep learning methods for multiagent learning
	• Transfer learning in multiagent settings
	• Applications of multiagent learning to real-world systems

The purpose of this symposium is to bring together researchers from machine learning, control, neuroscience, robotics, and multi-agent learning/planning communities to discuss how to broaden the scope of multi-agent learning research and address the fundamental issues that hinder the applicability of multi-agent learning for solving complex real world problems.

Submission Instructions and additional information can be found on our website: http://miaoliu.scripts.mit.edu/SSS-16 <http://miaoliu.scripts.mit.edu/SSS-16>

Organizing Committee:

Christopher Amato, University of New Hampshire
Miao Liu, MIT
Frans Oliehoek, University of Amsterdam / University of Liverpool
Karl Tuyls, University of Liverpool
Jonathan How, MIT
Peter Stone, University of Texas at Austin