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
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.- Anthology ID:
- E17-2077
- Volume:
- 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
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 480–484
- Language:
- URL:
- https://aclanthology.org/E17-2077
- DOI:
- Cite (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.
- Cite (Informal):
- Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents (Keizer et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/E17-2077.pdf