Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication

摘要

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. Learning systems that encompass perception, action and communication in a unified and principled way are still rare. 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. MLIS-2013 aims to bring researchers from multiple disciplines together that are in some way or another affected by the gap between perception, action and communication. Our goal is to provide a forum for interdisciplinary discussion that allows researchers to look at their work from new perspectives that go beyond their core community and develop new interdisciplinary collaborations.