Mihai Dobre


Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents
Simon Keizer | Markus Guhe | Heriberto Cuayáhuitl | Ioannis Efstathiou | Klaus-Peter Engelbrecht | Mihai Dobre | Alex Lascarides | Oliver Lemon
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

In this paper we present a comparative evaluation of various negotiation strategies within an online version of the game “Settlers of Catan”. The comparison is based on human subjects playing games against artificial game-playing agents (‘bots’) which implement different negotiation dialogue strategies, using a chat dialogue interface to negotiate trades. Our results suggest that a negotiation strategy that uses persuasion, as well as a strategy that is trained from data using Deep Reinforcement Learning, both lead to an improved win rate against humans, compared to previous rule-based and supervised learning baseline dialogue negotiators.