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The Challenge of Negotiation in the Game of Diplomacy

  • Dave de JongeEmail author
  • Tim Baarslag
  • Reyhan Aydoğan
  • Catholijn Jonker
  • Katsuhide Fujita
  • Takayuki Ito
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11327)

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.

Notes

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.

References

  1. 1.
    Aydoğan, R., Fujita, K., Baarslag, T., Jonker, C.M., Ito, T.: ANAC 2017: repeated multilateral negotiation league. In: The 11th International Workshop on Automated Negotiation, ACAN 2018 (2018)Google Scholar
  2. 2.
    Aydoğan, R., et al.: A baseline for nonlinear bilateral negotiations: the full results of the agents competing in ANAC 2014, pp. 96–122. Bentham Science Publishers (2017)Google Scholar
  3. 3.
    Baarslag, T., Aydoğan, R., Hindriks, K.V., Fuijita, K., Ito, T., Jonker, C.M.: The automated negotiating agents competition, 2010–2015. AI Mag. 36(4), 115–118 (2015)CrossRefGoogle Scholar
  4. 4.
    Baarslag, T., Hindriks, K., Jonker, C., Kraus, S., Lin, R.: The first Automated Negotiating Agents Competition (ANAC 2010). In: Ito, T., Zhang, M., Robu, V., Fatima, S., Matsuo, T. (eds.) New Trends in Agent-Based Complex Automated Negotiations. SCI, vol. 383, pp. 113–135. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-24696-8_7CrossRefGoogle Scholar
  5. 5.
    Fabregues, A.: Facing the challenge of human-aware negotiation. Ph.D. thesis, Universitat Autònoma de Barcelona (2012)Google Scholar
  6. 6.
    Fabregues, A., Sierra, C.: DipGame: a challenging negotiation testbed. Eng. Appl. Artif. Intell. 24(7), 1137–1146 (2011)CrossRefGoogle Scholar
  7. 7.
    Ferreira, A., Lopes Cardoso, H., Reis, L.P.: DipBlue: a diplomacy agent with strategic and trust reasoning. In: ICAART 2015 - Proceedings of the International Conference on Agents and Artificial Intelligence, Lisbon, Portugal, 10–12 January 2015, vol. 1, pp. 54–65. SciTePress (2015)Google Scholar
  8. 8.
    Fujita, K., Aydoğan, R., Baarslag, T., Ito, T., Jonker, C.: The fifth Automated Negotiating Agents Competition (ANAC 2014). In: Fukuta, N., Ito, T., Zhang, M., Fujita, K., Robu, V. (eds.) Recent Advances in Agent-based Complex Automated Negotiation. SCI, vol. 638, pp. 211–224. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-30307-9_13CrossRefGoogle Scholar
  9. 9.
    de Jonge, D., Sierra, C.: NB3: a multilateral negotiation algorithm for large, non-linear agreement spaces with limited time. Auton. Agent. Multi-Agent Syst. 29(5), 896–942 (2015)CrossRefGoogle Scholar
  10. 10.
    de Jonge, D., Sierra, C.: D-Brane: a diplomacy playing agent for automated negotiations research. Appl. Intell. 47(1), 158–177 (2017)CrossRefGoogle Scholar
  11. 11.
    de Jonge, D., Zhang, D.: Automated negotiations for general game playing. In: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2017, São Paulo, Brazil, 8–12 May 2017, pp. 371–379. ACM (2017)Google Scholar
  12. 12.
    Marinheiro, J., Lopes Cardoso, H.: Towards general cooperative game playing. In: Nguyen, N.T., Kowalczyk, R., van den Herik, J., Rocha, A.P., Filipe, J. (eds.) Transactions on Computational Collective Intelligence XXVIII. LNCS, vol. 10780, pp. 164–192. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-78301-7_8CrossRefGoogle Scholar
  13. 13.
    Mell, J., Gratch, J., Baarslag, T., Aydoğan, R., Jonker, C.: Results of the first annual human-agent league of the automated negotiating agents competition. In: Proceedings of the 2018 International Conference on Intelligent Virtual Agents (2018)Google Scholar
  14. 14.
    Ephrati, E., Kraus, S., Lehman, D.: An automated diplomacy player. In: Levy, D., Beal, D. (eds.) Heuristic Programming in Artificial Intelligence: The 1st Computer Olympia, pp. 134–153. Ellis Horwood Limited, Chicester (1989)Google Scholar
  15. 15.
    Silver, D., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Dave de Jonge
    • 1
    • 2
    Email author
  • Tim Baarslag
    • 3
  • Reyhan Aydoğan
    • 4
  • Catholijn Jonker
    • 5
  • Katsuhide Fujita
    • 6
  • Takayuki Ito
    • 7
  1. 1.IIIA-CSICBellaterraSpain
  2. 2.Western Sydney UniversitySydneyAustralia
  3. 3.Centrum Wiskunde & InformaticaAmsterdamThe Netherlands
  4. 4.Özyeğin UniversityIstanbulTurkey
  5. 5.Delft University of TechnologyDelftThe Netherlands
  6. 6.Tokyo University of Agriculture and TechnologyFuchuJapan
  7. 7.Nagoya Institute of TechnologyNagoyaJapan

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