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 strat- egy 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.
Original language | English |
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Title of host publication | Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics |
Subtitle of host publication | Volume 2, Short Papers |
Publisher | Association for Computational Linguistics |
Pages | 480-484 |
Number of pages | 5 |
ISBN (Print) | 9781945626357 |
Publication status | Published - 1 Apr 2017 |
Event | 15th Conference of the European Chapter of the Association for Computational Linguistics 2017 - Valencia, Spain Duration: 3 Apr 2017 → 7 Apr 2017 |
Conference
Conference | 15th Conference of the European Chapter of the Association for Computational Linguistics 2017 |
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Abbreviated title | EACL 2017 |
Country | Spain |
City | Valencia |
Period | 3/04/17 → 7/04/17 |
Keywords
- Dialogue systems
- Machine learning
- Negotiation
ASJC Scopus subject areas
- Artificial Intelligence