Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents

Simon Keizer, Markus Guhe, Heriberto Cuayahuitl, Ioannis Efstathiou, Klaus-Peter Engelbrecht, Mihai Dobre, Alex Lascarides, Oliver Lemon

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

14 Citations (Scopus)
9 Downloads (Pure)

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 languageEnglish
Title of host publicationProceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics
Subtitle of host publicationVolume 2, Short Papers
PublisherAssociation for Computational Linguistics
Pages480-484
Number of pages5
ISBN (Print)9781945626357
Publication statusPublished - 1 Apr 2017
Event15th Conference of the European Chapter of the Association for Computational Linguistics 2017 - Valencia, Spain
Duration: 3 Apr 20177 Apr 2017

Conference

Conference15th Conference of the European Chapter of the Association for Computational Linguistics 2017
Abbreviated titleEACL 2017
CountrySpain
CityValencia
Period3/04/177/04/17

Keywords

  • Dialogue systems
  • Machine learning
  • Negotiation

ASJC Scopus subject areas

  • Artificial Intelligence

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