Intelligent systems or robots that interact with their environment by perceiving, acting or communicating often face a challenge in how to bring these different concepts together. One of the main reasons for this challenge is the fact that the core concepts in perception, action and communication are typically studied by different communities: the computer vision, robotics and natural language processing communities, among others, without much interchange between them. As machine learning lies at the core of these communities, it can act as a unifying factor in bringing the communities closer together. Unifying these communities is highly important for understanding how state-of-the-art approaches from different disciplines can be combined (and applied) to form generally interactive intelligent systems.

The goal of this workshop is to bring researchers from multiple disciplines together who are in one way or another affected by the gap between action, perception and communication that typically exists for interactive systems or robots. Topics of interest include, but are not limited to:

Machine Learning:
- Reinforcement Learning
- Supervised Learning
- Unsupervised Learning
- Active Learning
- Learning from human feedback
- Learning from teaching, tutoring, instruction and demonstration
- Combinations or generalisations of the above

Interactive Systems:
- (Socially) Interactive Robotics
- Embodied Virtual Agents
- Avatars
- Multimodal systems
- Cognitive (robotics) architectures

Types of Communication:
- System interacting with a single human user
- System interacting with multiple human users
- System interacting with the environment
- System interacting with other machines

Submissions can take two forms. Long papers should not exceed 6 pages, and short papers should not exceed 3 pages. They should follow the AAAI submission format. A LaTeX package can be downloaded here: http://www.aaai.org/Publications/Template/AuthorKit.zip

Submissions should be made through the following EasyChair link: https://www.easychair.org/conferences/?conf=mlis2014

An overview of machine learning topics for interactive system can be found here: http://dl.acm.org/citation.cfm?id=2493530

Invited Speakers:
Andrea Thomaz, Georgia Institute of Technology, USA
Joelle Pineau, McGill University, Montréal, Canada
Matthias Scheutz, Tufts University, USA
Sonia Chernova, Worchester Polytechnic Institute, USA

Organising Committee:
Heriberto Cuayáhuitl, Heriot-Watt University, Edinburgh, UK
Lutz Frommberger, University of Bremen, Germany
Nina Dethlefs, Heriot-Watt University, Edinburgh, UK
Martijn van Otterlo, Radboud University Nijmegen, The Netherlands

Programme Committee:
Martin Butz, University of Tübingen, Germany
Crystal Chao, Georgia Institute of Technology, USA
Xiaoping Chen, University of Science and Technology China
Paul Crook, Microsoft Research, USA
Milica Gasic, University of Cambridge, UK
Jesse Hoey, University of Waterloo, Canada
Filip Jurcícek, Charles University in Prague, Czech Republic
Kazunori Komatani, Nagoya University, Japan
George Konidaris, MIT, USA
Ramon Lopez de Mantaras, Spanish Council for Scientific Research, Spain
Eduardo Morales, National Institute of Astrophysics, Optics and Electronics, Mexico
Plinio Moreno, Instituto Superior Técnico, Portugal
Justus Piater, University of Innsbruck, Austria
Olivier Pietquin, Supelec, France
Matthew Purver, Queen Mary University of London, UK
Alex Rudnicky, Carnegie Mellon University, USA
Jason Williams, Microsoft Research
Marco Wiering, University of Groningen, The Netherlands

Contact: organizers@mlis-workshop.org