We build dialogue system policies for negotiation, and in particular for argumentation. These dialogue policies are designed for negotiation against users of different cultural norms (individualists, collectivists, and altruists). In order to learn these policies we build simulated users (SUs), i.e. models that simulate the behavior of real users, and use Reinforcement Learning (RL). The SUs are trained on a spoken dialogue corpus in a negotiation domain, and then tweaked towards a particular cultural norm using hand-crafted rules. We evaluate the learned policies in a simulation setting. Our results are consistent with our SUs, in other words, the policies learn what they are designed to learn, which shows that RL is a promising technique for learning policies in domains, such as argumentation, that are more complex than standard slot-filling applications.
Cite as: Georgila, K., Traum, D. (2011) Reinforcement learning of argumentation dialogue Policies in negotiation. Proc. Interspeech 2011, 2073-2076, doi: 10.21437/Interspeech.2011-544
@inproceedings{georgila11_interspeech,
author={Kallirroi Georgila and David Traum},
title={{Reinforcement learning of argumentation dialogue Policies in negotiation}},
year=2011,
booktitle={Proc. Interspeech 2011},
pages={2073--2076},
doi={10.21437/Interspeech.2011-544}
}