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

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

Content Provider	ACM Digital Library
Author	Lefèvre, Fabrice Ferreira, Emmanuel
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 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.
Starting Page	61
Ending Page	69
Page Count	9
File Format	PDF
ISBN	9781450320191
DOI	10.1145/2493525.2493535
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-08-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Reward shaping Social signals User adaptation Value function approximation Reinforcement learning Dialogue management
Content Type	Text
Resource Type	Article

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