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  1. Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems (MLIS '13)
  2. Machine learning for interactive systems and robots: a brief introduction
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Learning machines that perceive, act and communicate
Transfer learning with applications on text, sensors and images
Robots, skills, and symbols
Developmental robotics at Aldebaran A-Lab
Open knowledge for human-robot interaction
Machine learning for interactive systems and robots: a brief introduction
Expectation propagation learning of finite Beta-Liouville mixtures for spatio-temporal object recognition
Homogeneity analysis for object-action relation reasoning in kitchen scenarios
Shared control of a robot using EEG-based feedback signals
Automatic interface optimization through random exploration of available elements
Social signal and user adaptation in reinforcement learning-based dialogue management
Inverse reinforcement learning for interactive systems

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Machine learning for interactive systems and robots: a brief introduction

Content Provider ACM Digital Library
Author CuayƔhuitl, Heriberto Dethlefs, Nina van Otterlo, Martijn Frommberger, Lutz
Abstract Research on interactive systems and robots, i.e. interactive machines that perceive, act and communicate, has applied a multitude of different machine learning frameworks in recent years, many of which are based on a form of reinforcement learning (RL). In this paper, we will provide a brief introduction to the application of machine learning techniques in interactive learning systems. We identify several dimensions along which interactive learning systems can be analyzed. We argue that while many applications of interactive machines seem different at first sight, sufficient commonalities exist in terms of the challenges faced. By identifying these commonalities between (learning) approaches, and by taking interdisciplinary approaches towards the challenges, we anticipate more effective design and development of sophisticated machines that perceive, act and communicate in complex, dynamic and uncertain environments.
Starting Page 19
Ending Page 28
Page Count 10
File Format PDF
ISBN 9781450320191
DOI 10.1145/2493525.2493530
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-08-04
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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