Learning mixed initiative dialog strategies by using reinforcement learning on both conversants

Michael S. English, Peter A. Heeman

Research output: Contribution to conferencePaper

23 Scopus citations

Abstract

This paper describes an application of reinforcement learning to determine a dialog policy for a complex collaborative task where policies for both the system and a proxy for a user of the system are learned simultaneously. With this approach a useful dialog policy is learned without the drawbacks of other approaches that require significant human interaction. The specific task that the agents were trained on was chosen for its complexity and requirement that both conversants bring task knowledge to the interaction, thus ensuring its collaborative nature. The results of our experiment show that you can use reinforcement learning to create an effective dialog policy, which employs a mixed initiative strategy, without the drawbacks of large amounts of data or significant human input.

Other

OtherHuman Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, HLT/EMNLP 2005, Co-located with the 2005 Document Understanding Conference, DUC and the 9th International Workshop on Parsing Technologies, IWPT
CountryCanada
CityVancouver, BC
Period10/6/0510/8/05

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

Fingerprint Dive into the research topics of 'Learning mixed initiative dialog strategies by using reinforcement learning on both conversants'. Together they form a unique fingerprint.

  • Cite this

    English, M. S., & Heeman, P. A. (2005). Learning mixed initiative dialog strategies by using reinforcement learning on both conversants. 1011-1018. Paper presented at Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, HLT/EMNLP 2005, Co-located with the 2005 Document Understanding Conference, DUC and the 9th International Workshop on Parsing Technologies, IWPT, Vancouver, BC, Canada. https://doi.org/10.3115/1220575.1220702