Paper: Mixture Model POMDPs for Efficient Handling of Uncertainty in Dialogue Management

ACL ID P08-2019
Title Mixture Model POMDPs for Efficient Handling of Uncertainty in Dialogue Management
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2008
Authors

In spoken dialogue systems, Partially Observ- able Markov Decision Processes (POMDPs) provide a formal framework for making di- alogue management decisions under uncer- tainty, but efficiency and interpretability con- siderations mean that most current statistical dialogue managers are only MDPs. These MDP systems encode uncertainty explicitly in a single state representation. We formalise such MDP states in terms of distributions over POMDP states, and propose a new di- alogue system architecture (Mixture Model POMDPs) which uses mixtures of these dis- tributions to efficiently represent uncertainty. We also provide initial evaluation results (with real users) for this architecture.