Machine learning for interactive systems and robots: a brief introduction

  • Authors:
  • Heriberto Cuayáhuitl;Martijn van Otterlo;Nina Dethlefs;Lutz Frommberger

  • Affiliations:
  • Heriot-Watt University, Edinburgh, United Kingdom;Radboud University Nijmegen, Nijmegen, The Netherlands;Heriot-Watt University, Edinburgh, United Kingdom;University of Bremen, Bremen, Germany

  • Venue:
  • Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication
  • Year:
  • 2013

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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.