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Building Adaptive Dialogue Systems Via Bayes-Adaptive POMDPs

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3 Author(s)
Shaowei Png ; Sch. of Comput. Sci., McGill Univ., Montreal, QC, Canada ; Pineau, J. ; Chaib-draa, B.

Recent research has shown that effective dialogue management can be achieved through the Partially Observable Markov Decision Process (POMDP) framework. However past research on POMDP-based dialogue systems usually assumed the parameters of the decision process were known a priori. The main contribution of this paper is to present a Bayesian reinforcement learning framework for learning the POMDP parameters online from data, in a decision-theoretic manner. We discuss various approximations and assumptions which can be leveraged to ensure computational tractability, and apply these techniques to learning observation models for several simulated spoken dialogue domains.

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Selected Topics in Signal Processing, IEEE Journal of  (Volume:6 ,  Issue: 8 )