COM 2016 - Symposium on CHALLENGES AND OPPORTUNITIES IN MULTIAGENT LEARNING FOR THE REAL WORLD

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 methods from different areas of AI.
Topics
Topics of interest include the following:
Learning in sequential settings and 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 versus 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