Google Research

Buildling adaptive dialogue systems via Bayes-adaptive POMDP

  • Shaowei Png
  • Joelle Pineau
  • B. Chaib-draa
IEEE Journal of Selected Topics in Signal Processing, vol. vol.6(8). 2012. (2012), pp. 917-927


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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