CFP: IJCAI Workshop on Machine Learning for Interactive Systems (abstract submission: April 13)


IJCAI Workshop on Machine Learning for Interactive Systems (MLIS’13):
Bridging the Gap between Perception, Action and Communication

August 3-4, 2013, Beijing, China

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
– Semi-Supervised 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

Example applications could include: (1) a robot may learn to
coordinate its speech with its actions, taking into account
visual feedback during their execution; (2) an autonomous car
may learn to coordinate its acceleration and steering behaviours
depending on observations of obstacles; (3) a team of robots
playing soccer may learn to coordinate their ball kicks depending
on the dynamic locations of their opponents; (4) a sensorimotor
system may learn to drive a wheelchair through feedback from
visual signals of the environment; (5) a mobile robot may
interactively learn from human guidance how to manipulate objects
and move through a building, based on human feedback using
language, gestures and interactive dialogue; or (6) a multimodal
smart phone can adapt its input and output modalities to
the user’s goals, workload and surroundings.

Submissions can take two forms. Long papers should not exceed
8 pages, and short (position) papers should not exceed 4 pages.
They should follow the ACM SIG proceedings format (option 1):
All submissions should be anonymised for peer-review.

Submission link:

Accepted papers will be published by ACM International Conference
Proceedings Series under ISBN 978-1-4503-2019-1. The proceedings
of MLIS’13 will be available on the ACM digital library on the day
of the workshop.

Invited Speakers:
Prof. Dr. Martin Riedmiller, University of Freiburg
Talk: “Learning Machines that Perceive, Act and Communicate”
Prof. Dr. Olivier Pietquin, Supélec, France
Title: “Inverse Reinforcement Learning for Interactive Systems”

Important Dates:
April 13, Abstract registration
April 20, Paper submission deadline
May 20, Notification of acceptance
May 30, Camera-ready deadline
August 3-4, MLIS workshop

Organising Committee:
Heriberto Cuayahuitl, 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

For all enquires, please mail: