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. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Jonsdottir, G. R., Thorisson, K. R., & Nivel, E. (2008). Learning smooth, human-like turntaking in realtime dialogue. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5208 LNAI, pp. 162–175). https://doi.org/10.1007/978-3-540-85483-8_17
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