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
Journal of Artificial Intelligence Research
Sample-efficient batch reinforcement learning for dialogue management optimization
ACM Transactions on Speech and Language Processing (TSLP)
An adaptive dialogue system with online dialogue policy learning
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
A comparative study of reinforcement learning techniques on dialogue management
EACL '12 Proceedings of the Student Research Workshop at the 13th Conference of the European Chapter of the Association for Computational Linguistics
Online Complex Action Learning and User State Estimation for Adaptive Dialogue Systems
ICTAI '12 Proceedings of the 2012 IEEE 24th International Conference on Tools with Artificial Intelligence - Volume 01
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Adaptive Dialogue Systems can be seen as smart interfaces that typically use natural language (spoken or written) as a means of communication. They are being used in many applications, such as customer service, in-car interfaces, even in rehabilitation, and therefore it is essential that these systems are robust, scalable and quickly adaptable in order to cope with changing user or system needs or environmental conditions. Making Dialogue Systems adaptive means overcoming several challenges, such as scalability or lack of training data. Achieving adaptation online has thus been an even greater challenge. We propose to build such a system, that will operate in an Assistive Living Environment and provide its services as a coach to patients that need to perform rehabilitative exercises. We are currently in the process of developing it, using Robot Operating System on a robotic platform.