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ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
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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
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Tracking in Reinforcement Learning
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ICMI'06/IJCAI'07 Proceedings of the ICMI 2006 and IJCAI 2007 international conference on Artifical intelligence for human computing
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SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication
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This paper investigates the conditions under which cues from social signals can be used for user adaptation (or user tracking) of a learning agent. In this work we consider the case of the Reinforcement Learning (RL) of a dialogue management module. Social signals (gazes, postures, emotions, etc.) have an undeniable importance in human interactions and can be used as an additional and user-dependent (subjective) reinforcement signal during learning. In this paper, the Kalman Temporal Differences (KTD) framework is employed in combination with a potential-based shaping reward method to properly integrate the social information in the optimisation procedure and adapt the policy to user profiles. In a second step the ability of the method to track a new user profile (after self learning of the user or switch to a new user) is shown. Experiments carried out using a state-of-the-art goal-oriented dialogue management framework with simulations support our claims.