Learning Smooth, Human-Like Turntaking in Realtime Dialogue

  • Authors:

  • Gudny Ragna Jonsdottir;Kristinn R. Thorisson;Eric Nivel

  • Affiliations:

  • Center for Analysis & Design of Intelligent Agents & School of Computer Science, Reykjavik University, Reykjavik, Iceland IS-103;Center for Analysis & Design of Intelligent Agents & School of Computer Science, Reykjavik University, Reykjavik, Iceland IS-103;Center for Analysis & Design of Intelligent Agents & School of Computer Science, Reykjavik University, Reykjavik, Iceland IS-103

  • Venue:
  • IVA '08 Proceedings of the 8th international conference on Intelligent Virtual Agents
  • Year:
  • 2008

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Abstract

Giving synthetic agents human-like realtime turntaking skills is a challenging task. Attempts have been made to manually construct such skills, with systematic categorization of silences, prosody and other candidate turn-giving signals, and to use analysis of corpora to produce static decision trees for this purpose. However, for general-purpose turntaking skills which vary between individuals and cultures, a system that can learn them on-the-job would be best. We are exploring ways to use machine learning to have an agent learn proper turntaking during interaction. We have implemented a talking agent that continuously adjusts its turntaking behavior to its interlocutors based on realtime analysis of the other party's prosody. Initial results from experiments on collaborative, content-free dialogue show that, for a given subset of turn-taking conditions, our modular reinforcement learning techniques allow the system to learn to take turns in an efficient, human-like manner.