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(2014)
Article 12e, Publication date
Simon Keizer, M. Foster, Zhuoran Wang, Oliver Lemon (2014)
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Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication
12e Introduction to the Special Issue on Machine Learning for Multiple Modalities in Interactive Systems and Robots ´ HERIBERTO CUAYAHUITL, Heriot-Watt University, United Kingdom LUTZ FROMMBERGER, University of Bremen, Germany NINA DETHLEFS, Heriot-Watt University, United Kingdom ANTOINE RAUX, Lenovo Research, United States of America MATHEW MARGE, Carnegie Mellon University, United States of America HENDRIK ZENDER, Nuance Communications, Germany This special issue highlights research articles that apply machine learning to robots and other systems that interact with users through more than one modality, such as speech, gestures, and vision. For example, a robot may coordinate its speech with its actions, taking into account (audio-)visual feedback during their execution. Machine learning provides interactive systems with opportunities to improve performance not only of individual components but also of the system as a whole. However, machine learning methods that encompass multiple modalities of an interactive system are still relatively hard to find. The articles in this special issue represent examples that contribute to filling this gap. Categories and Subject Descriptors: I.2.6 [Artificial Intelligence]: Learning--Interactive learning, supervised learning, reinforcement learning, multiclass learning, unsupervised learning; I.2.7 [Artificial Intelligence]: Natural Language Processing--Conversational interfaces; I.2.9 [Artificial Intelligence]: Robotics--Human-robot interaction General Terms: Theory, Algorithms, Design, Experimentation, Performance
ACM Transactions on Interactive Intelligent Systems (TiiS) – Association for Computing Machinery
Published: Oct 14, 2014
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