@InProceedings{keizer-EtAl:2017:EACLshort,
  author    = {Keizer, Simon  and  Guhe, Markus  and  Cuayahuitl, Heriberto  and  Efstathiou, Ioannis  and  Engelbrecht, Klaus-Peter  and  Dobre, Mihai  and  Lascarides, Alex  and  Lemon, Oliver},
  title     = {Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {480--484},
  abstract  = {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.},
  url       = {http://www.aclweb.org/anthology/E17-2077}
}