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, Alexandra Lascarides, Oliver Lemon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationProceedings of the European Chapter of the Association for Computational Linguistics (EACL 2017)
PublisherAssociation for Computational Linguistics
Pages480-484
Number of pages5
ISBN (Print)978-1-945626-34-0
Publication statusPublished - 7 Apr 2017
Event15th EACL 2017 Software Demonstrations - Valencia, Spain
Duration: 3 Apr 20177 Apr 2017
http://eacl2017.org/
http://eacl2017.org/index.php

Conference

Conference15th EACL 2017 Software Demonstrations
Abbreviated titleEACL 2017
Country/TerritorySpain
CityValencia
Period3/04/177/04/17
Internet address

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