Williams, JD and Poupart, P and Young, S (2005) Partially Observable Markov Decision Processes with continuous observations for dialogue management. Proceedings of the 6th SIGdial Workshop on Discourse and Dialogue. pp. 25-34.Full text not available from this repository.
This work shows how a dialogue model can be represented as a Partially Observable Markov Decision Process (POMDP) with observations composed of a discrete and continuous component. The continuous component enables the model to directly incorporate a confidence score for automated planning. Using a testbed simulated dialogue management problem, we show how recent optimization techniques are able to find a policy for this continuous POMDP which outperforms a traditional MDP approach. Further, we present a method for automatically improving handcrafted dialogue managers by incorporating POMDP belief state monitoring, including confidence score information. Experiments on the testbed system show significant improvements for several example handcrafted dialogue managers across a range of operating conditions.
|Divisions:||Div F > Machine Intelligence|
|Depositing User:||Cron job|
|Date Deposited:||16 Jul 2015 14:09|
|Last Modified:||02 Aug 2015 01:36|