Exploiting structure to efficiently solve large scale partially observable markov decision processes
Exploiting structure to efficiently solve large scale partially observable markov decision processes
The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management
Computer Speech and Language
Bayesian update of dialogue state: A POMDP framework for spoken dialogue systems
Computer Speech and Language
Representing uncertainty about complex user goals in statistical dialogue systems
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Hi-index | 0.00 |
This paper presents the first demonstration of a statistical spoken dialogue system that uses automatic belief compression to reason over complex user goal sets. Reasoning over the power set of possible user goals allows complex sets of user goals to be represented, which leads to more natural dialogues. The use of the power set results in a massive expansion in the number of belief states maintained by the Partially Observable Markov Decision Process (POMDP) spoken dialogue manager. A modified form of Value Directed Compression (VDC) is applied to the POMDP belief states producing a near-lossless compression which reduces the number of bases required to represent the belief distribution.