• 9 May 2016: the ALA proceedings are now available to download
• 5 April 2016: the preliminary program is now online!
• 18 March 2016: we are happy to announce our invited speaker for this year, dr. Peter Vamplew.
• 9 February 2016: Submission is now closed, we are happy to have received 21 papers this year!
• 21 January 2016: submission deadline extended to February 8!
• 18 November 2015: ALA 2016 site launched

The ALA social event will be held on Monday 9 May, starting at 7:30pm at The Quay Kitchen and Bar. The bar serves food as well, for those in need of dinner.

Adaptive Learning Agents (ALA) encompasses diverse fields such as Computer Science, Software Engineering, Biology, as well as Cognitive and Social Sciences. The ALA workshop will focus on agents and multiagent systems which employ learning or adaptation.

This workshop is a continuation of the long running AAMAS series of workshops on adaptive agents, now in its fourteenth year. Previous editions of this workshop may be found at the following urls:

• ALA-15
• ALA-14
• ALA-13
• ALA-12
• ALA-11
• ALA-10
• ALA-09
• ALAMAS+ALAg-08
• ALAg-07
• Earlier editions

The goal of this workshop is to increase awareness and interest in adaptive agent research, encourage collaboration and give a representative overview of current research in the area of adaptive and learning agents and multiagent systems. It aims at bringing together not only scientists from different areas of computer science (e.g., agent architectures, reinforcement learning, and evolutionary algorithms) but also from different fields studying similar concepts (e.g., game theory, bio-inspired control, mechanism design).

The workshop will serve as an inclusive forum for the discussion on ongoing or completed work in both theoretical and practical issues of adaptive and learning agents and multiagent systems.

This workshop will focus on all aspects of adaptive and learning agents and multiagent systems with a particular amphasis on how to modify established learning techniques and/or create new learning paradigms to address the many challenges presented by complex real-world problems. The topics of interest include but are not limited to:

• Novel combinations of reinforcement and supervised learning approaches
• Integrated learning approaches that work with other agent reasoning modules like negotiation, trust models, coordination, etc.
• Supervised multiagent learning
• Reinforcement learning (single and multiagent)
• Planning (single and multiagent)
• Reasoning (single and multiagent)
• Distributed learning
• Adaptation and learning in dynamic environments
• Evolution of agents in complex environments
• Co-evolution of agents in a multiagent setting
• Cooperative exploration and learning to cooperate and collaborate
• Learning trust and reputation
• Communication restrictions and their impact on multiagent coordination
• Design of reward structure and fitness measures for coordination
• Scaling learning techniques to large systems of learning and adaptive agents
• Emergent behaviour in adaptive multiagent systems
• Game theoretical analysis of adaptive multiagent systems
• Neuro-control in multiagent systems
• Bio-inspired multiagent systems
• Applications of adaptive and learning agents and multiagents systems to real world complex systems

Accepted papers from the workshop will be eligible to be extended for inclusion in a special issue journal.

• Submission Deadline: February 8, 2016
• Notification of acceptance: March 7, 2016
• Camera-ready copies: March 10, 2016
• Workshop: May 9/10, 2016

The ALA proceedings can be downloaded here.

Monday 9 May

Tuesday 10 May

Dr. Peter Vamplew Federation University Australia Website Bio: Peter Vamplew is an Associate Professor in Information Technology at Federation University Australia, and co-leader of the Federation Learning Agents Group. Over the last decade he has been one of the leading researchers driving the emergence of MORL as a distinct and important sub-area of reinforcement learning research.

Preventing the Robot Apocalypse
(and other reasons that we need multiobjective reinforcement learning)

Abstract: While traditional reinforcement learning approaches assume a scalar reward, many real-world problems actually exhibit multiple, conflicting objectives. Multiobjective reinforcement learning (MORL) is an emerging area of research, aimed at extending reinforcement learning methods to address tasks with multiple objectives. This presentation will provide an overview of MORL, starting from a definition of the formalisms underpinning MORL, and using three general scenarios to illustrate situations where scalar reinforcement learning methods may not be applicable. In particular the task of developing ethically-constrained intelligent agents will be considered as a driving case for the development of MORL methods. The state-of-the-art in MORL algorithms will also be reviewed, providing guidance on existing approaches for practitioners, and identifying remaining challenges for MORL research.

• Adrian Agogino, University of California Santa Cruz, USA
• Nolan Bard, University of Alberta, CAN
• Enda Barrett, National University of Ireland, Galway, IRL
• Haitham Bou Ammar, Princeton University, USA
• Bastian Bröcker, University of Liverpool, UK
• Jen Jen Chung, Oregon State University, USA
• Daniel Claes, University of Liverpool, UK
• William Curran, Oregon State University, USA
• Sam Devlin, University of York, UK
• Kyriakos Efthymiadis, University of York, UK
• Joscha Fossel, University of Liverpool, UK
• Anna Harutyunyan, VU Brussel, BE
• Daniel Hennes, DFKI, DE
• Enda Howley, National University of Ireland, Galway, IRL
• Michael Kaisers, CWI, NL
• Sepideh Kharaghani, Oregon State University, USA
• Matt Knudson, NASA Ames Research Center, USA
• Patrick MacAlpine, University of Texas at Austin, USA
• Kleanthis Malialis, University College London, UK
• Carrie Rebhuhn, Oregon State University, USA
• Kaushik Subramanian, Georgia Institute of Technology, USA
• Peter Vrancx, VU Brussel, BE

• Daan Bloembergen (University of Liverpool, UK)
• Tim Brys (Vrije Universiteit Brussels, Belgium)
• Logan Yliniemi (University of Nevada, Reno, USA)

• Daniel Kudenko (University of York, UK)
• Ann Nowé (Vrije Universiteit Brussels, Belgium)
• Sandip Sen (University of Tulsa, USA)
• Peter Stone (University of Texas at Austin, USA)
• Matthew Taylor (Washington State University, USA)
• Kagan Tumer (Oregon State University, USA)
• Karl Tuyls (University of Liverpool, UK)

If you have any questions about the ALA workshop, please contact the organizers at:
ala.workshop.2016 AT gmail.com

For more general news, discussion, collaboration and networking opportunities with others interested in Adaptive Learning Agents then please join our Linkedin Group: