MLIS 2013 - Workshop on Machine Learning for Interactive Systems (MLIS2013)

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, the following:
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
Examples 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 users goals, workload and surroundings.
Submission will be possible through an EasyChair link provided a bit closer to the submission time.
Accepted papers will be published in a book with an ISSN, possibly in a Springer Verlag Lecture Notes in Artificial Intelligence, or SpringerBriefs, depending on the number and quality of submissions we receive.