Machine Learning for Interactive Systems: Bridging the Gap between Perception, Action and Communication
Papers from the 2014 AAAI Workshop
Heriberto Cuayáhuitl, Lutz Frommberger, Nina Dethlefs, Martijn van Otterlo, Workshop Organizers
Technical Report WS-14-07
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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.