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Carnegie Mellon Robotics Institute

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Abstract

Spoken dialogue managers have benefited from using stochastic planners such as Markov Decision Processes (MDPs). However, so far, MDPs do not handle well noisy and ambiguous speech utterances. We use a Partially Observable Markov Decision Process (POMDP)-style approach to generate dialogue strategies by inverting the notion of dialogue state; the state represents the user's intentions, rather than the system state. We demonstrate that under the same noisy conditions, a POMDP dialogue manager makes fewer mistakes than an MDP dialogue manager. Furthermore, as the quality of speech recognition degrades, the POMDP dialogue manager automatically adjusts the policy.

Keywords

POMDP, dialogue, robotics

Notes

Text Reference

Nicholas Roy, Joelle Pineau, and Sebastian Thrun, "Spoken Dialog Management for Robots," Proceedings of the ACL 2000, October, 2000.

BibTeX Reference

@inproceedings{Roy_2000_3456,
   author = "Nicholas Roy and Joelle Pineau and Sebastian Thrun",
   title = "Spoken Dialog Management for Robots",
   booktitle = "Proceedings of the ACL 2000",
   month = "October",
   year = "2000",
}

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