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The Challenge of Negotiation in the Game of Diplomacy

  • Dave de Jonge
  • , Tim Baarslag
  • , Reyhan Aydoğan
  • , Catholijn Jonker
  • , Katsuhide Fujita
  • , Takayuki Ito

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

The game of Diplomacy has been used as a test case for complex automated negotiations for a long time, but to date very few successful negotiation algorithms have been implemented for this game. We have therefore decided to include a Diplomacy tournament within the annual Automated Negotiating Agents Competition (ANAC). In this paper we present the setup and the results of the ANAC 2017 Diplomacy Competition and the ANAC 2018 Diplomacy Challenge. We observe that none of the negotiation algorithms submitted to these two editions have been able to significantly improve the performance over a non-negotiating baseline agent. We analyze these algorithms and discuss why it is so hard to write successful negotiation algorithms for Diplomacy. Finally, we provide experimental evidence that, despite these results, coalition formation and coordination do form essential elements of the game.
Original languageEnglish
Title of host publicationAgreement Technologies - 6th International Conference, AT 2018, Revised Selected Papers
EditorsM. Lujak
PublisherSpringer Verlag
Pages100-114
ISBN (Print)9783030172930
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event6th International Conference on Agreement Technologies, AT 2018 - Bergen, Norway
Duration: 6 Dec 20187 Dec 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Conference on Agreement Technologies, AT 2018
Country/TerritoryNorway
CityBergen
Period6/12/187/12/18

Funding

Acknowledgments. This work is part of the Veni research programme with project number 639.021.751, which is financed by the Netherlands Organisation for Scientific Research (NWO), and project LOGISTAR, funded by the E.U. Horizon 2020 research and innovation programme, Grant Agreement No. 769142. This work is part of the Veni research programme with project number 639.021.751, which is financed by the Netherlands Organisation for Scientific Research (NWO), and project LOGISTAR, funded by the E.U. Horizon 2020 research and innovation programme, Grant Agreement No. 769142.

FundersFunder number
Horizon 2020
Horizon 2020 Framework Programme769142
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