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Social signal and user adaptation in reinforcement learning-based dialogue management

Abstract : 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 optimisa-tion 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.
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Submitted on : Friday, May 13, 2016 - 12:18:44 PM
Last modification on : Tuesday, January 14, 2020 - 10:38:06 AM




Emmanuel Ferreira, Fabrice Lefèvre. Social signal and user adaptation in reinforcement learning-based dialogue management. MLIS '13 Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication , Aug 2013, Beijing, China. ⟨10.1145/2493525.2493535⟩. ⟨hal-01315527⟩



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