In this paper, an original framework to model human-machine spoken dialogues is proposed to deal with co-adaptation between users and Spoken Dialogue Systems in non-cooperative tasks. The conversation is modeled as a Stochastic Game: both the user and the system have their own preferences but have to come up with an agreement to solve a non-cooperative task. They are jointly trained so the Dialogue Manager learns the optimal strategy against the best possible user. Results obtained by simulation show that non-trivial strategies are learned and that this framework is suitable for dialogue modeling.
Barlier et al. REF modeled non-cooperative task dialog as a stochastic game and learned jointly the strategies of both agents.
Human-Machine Dialogue as a Stochastic Game
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Computer Science