Machine Learning for Interactive Systems: Challenges and Future Trends

Olivier Pietquin 1, * Manuel Lopes 2
* Corresponding author
2 Flowers - Flowing Epigenetic Robots and Systems
Inria Bordeaux - Sud-Ouest, U2IS - Unité d'Informatique et d'Ingénierie des Systèmes
Abstract : Machine learning has been introduced more than 40 years ago in interactive systems through speech recognition or computer vision. Since that, machine learning gained in interest in the scientific community involved in human- machine interaction and raised in the abstraction scale. It moved from fundamental signal processing to language understanding and generation, emotion and mood recogni- tion and even dialogue management or robotics control. So far, existing machine learning techniques have often been considered as a solution to some problems raised by inter- active systems. Yet, interaction is also the source of new challenges for machine learning and offers new interesting practical but also theoretical problems to solve. In this paper, we address these challenges and describe why research in machine learning and interactive systems should converge in the future.
Document type :
Conference papers
Liste complète des métadonnées

Cited literature [82 references]  Display  Hide  Download
Contributor : Olivier Pietquin <>
Submitted on : Friday, October 10, 2014 - 6:38:13 PM
Last modification on : Thursday, February 21, 2019 - 10:52:46 AM
Document(s) archivé(s) le : Sunday, January 11, 2015 - 11:35:21 AM


Files produced by the author(s)


  • HAL Id : hal-01073947, version 1


Olivier Pietquin, Manuel Lopes. Machine Learning for Interactive Systems: Challenges and Future Trends. Workshop Affect, Compagnon Artificiel, Interaction (WACAI 2014), Jun 2014, Rouen, France. ⟨hal-01073947⟩



Record views


Files downloads