Learning agents for uncertain environments (extended abstract)
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Statistical methods for speech recognition
Statistical methods for speech recognition
Building natural language generation systems
Building natural language generation systems
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Data Mining Techniques in Speech Synthesis
Data Mining Techniques in Speech Synthesis
Face Detection and Gesture Recognition for Human-Computer Interaction
Face Detection and Gesture Recognition for Human-Computer Interaction
Algorithms for Inverse Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Reinforcement Learning Architecture for Web Recommendations
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
PARADISE: a framework for evaluating spoken dialogue agents
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
The Knowledge Engineering Review
Partially observable Markov decision processes for spoken dialog systems
Computer Speech and Language
Automatic learning of dialogue strategy using dialogue simulation and reinforcement learning
HLT '02 Proceedings of the second international conference on Human Language Technology Research
A general, abstract model of incremental dialogue processing
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Agenda-based user simulation for bootstrapping a POMDP dialogue system
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Training parsers by inverse reinforcement learning
Machine Learning
Gaussian processes for fast policy optimisation of POMDP-based dialogue managers
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Sample-efficient batch reinforcement learning for dialogue management optimization
ACM Transactions on Speech and Language Processing (TSLP)
The Hidden Agenda User Simulation Model
IEEE Transactions on Audio, Speech, and Language Processing
A probabilistic framework for dialog simulation and optimal strategy learning
IEEE Transactions on Audio, Speech, and Language Processing
Statistical user simulation for spoken dialogue systems: what for, which data, which future?
SDCTD '12 NAACL-HLT Workshop on Future Directions and Needs in the Spoken Dialog Community: Tools and Data
Laugh-aware virtual agent and its impact on user amusement
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Human machine interaction is a field where machine learning is present at almost any level, from human activity recognition to natural language generation. The interaction manager is probably one of the latest components of an interactive system that benefited from machine learning techniques. In the late 90's, sequential decision making algorithms like reinforcement learning have been introduced in the field with the aim of making the interaction more natural in a measurable way. Yet, these algorithms require providing the learning agent with a reward after each interaction. This reward is generally handcrafted by the system designer who introduces again some expertise in the system. In this paper, we will discuss a method for learning a reward function by observing expert humans, namely inverse reinforcement learning (IRL). IRL will then be applied to several steps of the spoken dialogue management design such as user simulation and clustering but also to co-adaptation of human user and machine.