In the last two decades, machine learning techniques have been explored extensively as a vital component to address challenges in multi-agent systems, which is known as Multiagent Learning. For example, many application domains are envisioned in which teams of software agents or robots learn to cooperate amongst each other and with human beings to achieve global objectives. Multiagent learning may also be essential in many non-cooperative scenarios such as negotiation and auction, where classical game-theoretic solutions are either infeasible or inappropriate.

Multiagent learning is an active field of research that deals with the problem of how agents can learn and adapt effectively in non-stationary environments where other coexisting agents are simultaneously learning and adapting. This is a fertile area of research that seems ripe for progress and we have witnessed numerous significant theoretical and practical developments in the last two decades. Large bodies of multiagent learning techniques have been developed to address the question of learning towards optimal solutions (e.g., Nash equilibrium, Pareto optimality and social optimality) against different types of partners (e.g., self-play, certain types of selfish players).

This workshop focuses on theory and practice in multi-agent learning in conjunction with International conference on agents (see http://www.itolab.nitech.ac.jp/ICA2016/organization/index.html). We would like to create a forum to discuss interesting results both theoretically and empirically related with multiagent learning. The goal of this workshop aims to bring together diverse viewpoints in multiagent leaerning in an attempt to consolidate the common ground, identify new lines of directions, sharing insights into recent results and common challenges, and ultimately promote the rapid advance of multiagent learning research community.

The workshop will cover a range of sub-topics (including but not limited to):
* Multiagent Reinforcement Learning (RL)
* Multiagent Adaptive Learning
* Multiagent Evolutionary Learning
* Theoretical aspects of Multiagent Learning
* Abstractions in Multiagent Learning
* Partial observable Multiagent RL
* Transfer Learning in Multiagent Learning
* Multiagent Bayesian RL
* Multiagent Deep RL
* Supervised Multiagent Learning
* Knowledge Representation in Multiagent Learning
* Empirical evaluations of Multiagent Learning
* Multiagent Hierarchical Learning
* Multiagent Learning in Negotiation and Auction
* Scaling learning techniques to large systems of learning and adaptive agent
* Emergent behaviour in adaptive multiagent systems
* Bio-inspired Multiagent Learning

Paper Submission
We encourage submission in the CPS format. You can find the format requirements on IEEE CPS website:http://www.computer.org/web/cs-cps/ . The format of the final manuscript should be no more than 6 pages. Submission is entirely automated by a paper management tool, which is available from the main web site:
https://easychair.org/conferences/?conf=ieee-ica2016
Authors must first register their own account by obtaining a password, and then follow the instructions. For more paper formatting information, please refer to http://www.itolab.nitech.ac.jp/ICA2016/submission/index.html for details.

All accepted papers will be provided an oral presentation and/or a poster. Each paper will be reviewed by at least three PC members or experts in the field. This is to facilitate a single-blind review. All accepted papers will appear in the Main Conference Proceedings to be published by CPS. They will be indexed by EI, SCOPUS, INSPEC, DBLP and Thomson ISI.

Important Dates
Submission Deadline: July 24th, 2016
Notification of acceptance: July 27th, 2016
Camera-ready copies: July 31th, 2016
Workshop: Sep 27th, 2016

Selected papers will be invited to submit to a special issue of one of the following journals:

Int’l Journal of Smart Computing and Artificial Intelligence
Information Engineering Express – International Journal
IEICE Transactions on Information and Systems

Organization
This year’s workshop is organized by
• Jianye Hao (Tianjin University, China)
• Siqi Chen (Southwest University, China)

Committee members:
• Bijan Ranjbar-Sahraei, TU Delft, Netherlands
• Shuang Zhou, Maastricht University, Netherlands
• Zan Cheng，Southwest University, China
• Yujing Hu, Ali Research, China
• Yifeng Zhou, Southeast University, China
• Yichuan Jiang, Southeast University, China
• Xu Wang, Nanjing University, China
• Feng Wu, The University of Science and Technology, China
• Yingke Chen, Sichuan University, China