Buildling adaptive dialogue systems via Bayes-adaptive POMDP
Venue
IEEE Journal of Selected Topics in Signal Processing, vol. vol.6(8). 2012. (2012), pp. 917-927
Publication Year
2012
Authors
Shaowei Png, Joelle Pineau, B. Chaib-draa
BibTeX
Abstract
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.
