Efficient model learning for dialog management

Intelligent planning algorithms such as the Partially Observable Markov Decision Process (POMDP) have succeeded in dialog management applications [10, 11, 12] because of their robustness to the inherent uncertainty of human interaction. Like all dialog planning systems, however, POMDPs require an accurate model of the user (the different states of the user, what the user might say, etc.). POMDPs are generally specified using a large probabilistic model with many parameters; these parameters are difficult to specify from domain knowledge, and gathering enough data to estimate the parameters accurately a priori is expensive. In this paper, we take a Bayesian approach to learning the user model simultaneously the dialog management problem. At the heart of our approach is an efficient incremental update algorithm that allows the dialog manager to replan just long enough to improve the current dialog policy given data from recent interactions. The update process has a relatively small comp...

• Type: Conference
• Year: 2007
• Where: HRI
• Authors: Finale Doshi, Nicholas Roy
• Keywords: Dialog, Dialog Management, Dialog Manager, HRI 2007, Human Computer Interaction