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Introduction to the Special Issue on Machine Learning for Multiple Modalities in Interactive Systems and Robots

Introduction to the Special Issue on Machine Learning for Multiple Modalities in Interactive... 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Interactive Intelligent Systems (TiiS) Association for Computing Machinery

Introduction to the Special Issue on Machine Learning for Multiple Modalities in Interactive Systems and Robots

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References (10)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2014 by ACM Inc.
ISSN
2160-6455
DOI
10.1145/2670539
Publisher site
See Article on Publisher Site

Abstract

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

Journal

ACM Transactions on Interactive Intelligent Systems (TiiS)Association for Computing Machinery

Published: Oct 14, 2014

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