@inproceedings{keizer-etal-2017-evaluating,
title = "Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents",
author = "Keizer, Simon and
Guhe, Markus and
Cuay{\'a}huitl, Heriberto and
Efstathiou, Ioannis and
Engelbrecht, Klaus-Peter and
Dobre, Mihai and
Lascarides, Alex and
Lemon, Oliver",
editor = "Lapata, Mirella and
Blunsom, Phil and
Koller, Alexander",
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-2077",
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.",
}
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%0 Conference Proceedings
%T Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents
%A Keizer, Simon
%A Guhe, Markus
%A Cuayáhuitl, Heriberto
%A Efstathiou, Ioannis
%A Engelbrecht, Klaus-Peter
%A Dobre, Mihai
%A Lascarides, Alex
%A Lemon, Oliver
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F keizer-etal-2017-evaluating
%X 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.
%U https://aclanthology.org/E17-2077
%P 480-484
Markdown (Informal)
[Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents](https://aclanthology.org/E17-2077) (Keizer et al., EACL 2017)
ACL
- Simon Keizer, Markus Guhe, Heriberto Cuayáhuitl, Ioannis Efstathiou, Klaus-Peter Engelbrecht, Mihai Dobre, Alex Lascarides, and Oliver Lemon. 2017. Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 480–484, Valencia, Spain. Association for Computational Linguistics.