Rethinking Frequency Opponent Modeling in Automated Negotiation

  • Okan Tunalı
  • Reyhan Aydoğan
  • Victor Sanchez-Anguix
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10621)


Frequency opponent modeling is one of the most widely used opponent modeling techniques in automated negotiation, due to its simplicity and its good performance. In fact, it outperforms even more complex mechanisms like Bayesian models. Nevertheless, the classical frequency model does not come without its own assumptions, some of which may not always hold in many realistic settings. This paper advances the state of the art in opponent modeling in automated negotiation by introducing a novel frequency opponent modeling mechanism, which soothes some of the assumptions introduced by classical frequency approaches. The experiments show that our proposed approach outperforms the classic frequency model in terms of evaluation of the outcome space, estimation of the Pareto frontier, and accuracy of both issue value evaluation estimation and issue weight estimation.


Agreement technologies Automated negotiation Opponent modeling Multi-agent systems 


  1. 1.
    Afiouni, E.N., Øvrelid, L.J.: Negotiation for strategic video games. Master’s thesis, NTNU (2013)Google Scholar
  2. 2.
    Alsrheed, F., El Rhalibi, A., Randles, M., Merabti, M.: Intelligent agents for automated cloud computing negotiation. In: IEEE International Conference on Multimedia Computing and Systems, pp. 1169–1174. IEEE (2014)Google Scholar
  3. 3.
    An, B., Gatti, N., Lesser, V.: Extending alternating-offers bargaining in one-to-many and many-to-many settings. In: Proceedings of the 2009 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, vol. 2, pp. 423–426 (2009)Google Scholar
  4. 4.
    Aydoğan, R., Baarslag, T., Hindriks, K.V., Jonker, C.M., Yolum, P.: Heuristics for using cp-nets in utility-based negotiation without knowing utilities. Knowl. Inf. Syst. 45(2), 357–388 (2015)CrossRefGoogle Scholar
  5. 5.
    Aydoğan, R., Festen, D., Hindriks, K.V., Jonker, C.M.: Alternating offers protocols for multilateral negotiation. In: Fujita, K., Bai, Q., Ito, T., Zhang, M., Ren, F., Aydoğan, R., Hadfi, R. (eds.) Modern Approaches to Agent-based Complex Automated Negotiation. SCI, vol. 674, pp. 153–167. Springer, Cham (2017). doi: 10.1007/978-3-319-51563-2_10 CrossRefGoogle Scholar
  6. 6.
    Aydoğan, R., Yolum, P.: Ontology-based learning for negotiation. In: IEEE/WIC/ACM International Conference on Intelligent Agent Technology, pp. 177–184 (2009)Google Scholar
  7. 7.
    Baarslag, T.: Measuring the performance of online opponent models. In: Exploring the Strategy Space of Negotiating Agents. ST, pp. 111–127. Springer, Cham (2016). doi: 10.1007/978-3-319-28243-5_6
  8. 8.
    Baarslag, T., Hendrikx, M., Hindriks, K., Jonker, C.: Predicting the performance of opponent models in automated negotiation. In: International Joint Conference on Web Intelligence and Intelligent Agent Technologies, vol. 2, pp. 59–66. IEEE (2013)Google Scholar
  9. 9.
    Baarslag, T., Hendrikx, M.J., Hindriks, K.V., Jonker, C.M.: Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques. Auton. Agent Multi Agent Syst. 30, 849–898 (2016)CrossRefGoogle Scholar
  10. 10.
    Buffett, S., Spencer, B.: Learning opponents’ preferences in multi-object automated negotiation. In: Proceedings of the 7th International Conference on Electronic Commerce, pp. 300–305 (2005)Google Scholar
  11. 11.
    Bui, H.H., Kieronska, D., Venkatesh, S.: Learning other agents’ preferences in multiagent negotiation. In: Proceedings of the National Conference on Artificial Intelligence, pp. 114–119 (1996)Google Scholar
  12. 12.
    Coehoorn, R.M., Jennings, N.R.: Learning an opponent’s preferences to make effective multi-issue negotiation tradeoffs. In: The 6th International Conference on E-Commerce, pp. 59–68 (2004)Google Scholar
  13. 13.
    Dirkzwager, A., Hendrikx, M.: An adaptive negotiation strategy for real-time bilateral negotiations. In: Marsa-Maestre, I., Lopez-Carmona, M.A., Ito, T., Zhang, M., Bai, Q., Fujita, K. (eds.) Novel Insights in Agent-based Complex Automated Negotiation. SCI, vol. 535, pp. 163–170. Springer, Tokyo (2014). doi: 10.1007/978-4-431-54758-7_10 CrossRefGoogle Scholar
  14. 14.
    Faratin, P., Sierra, C., Jennings, N.R.: Using similarity criteria to make issue trade-offs in automated negotiations. Artif. Intell. 142(2), 205–237 (2002)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Faratin, P., Sierra, C., Jennings, N.R.: Negotiation decision functions for autonomous agents. Robot. Auton. Syst. 24(3–4), 159–182 (1998)CrossRefGoogle Scholar
  16. 16.
    Fatima, S., Kraus, S., Wooldridge, M.: Principles of Automated Negotiation. Cambridge University Press, Cambridge (2014)CrossRefGoogle Scholar
  17. 17.
    Fatima, S.S., Wooldridge, M., Jennings, N.R.: An agenda-based framework for multi-issue negotiation. Artif. Intell. 152(1), 1–45 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    van Galen, L.N.: Agent smith: opponent model estimation in bilateral multi-issue negotiation. In: Ito, T., Zhang, M., Robu, V., Fatima, S., Matsuo, T. (eds.) New Trends in Agent-Based Complex Automated Negotiations. SCI, vol. 383, pp. 167–174. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-24696-8_12 CrossRefGoogle Scholar
  19. 19.
    Gerla, M., Lee, E.K., Pau, G., Lee, U.: Internet of vehicles: from intelligent grid to autonomous cars and vehicular clouds. In: IEEE World Forum on Internet of Things, pp. 241–246 (2014)Google Scholar
  20. 20.
    Hindriks, K., Jonker, C.M., Kraus, S., Lin, R., Tykhonov, D.: Genius: negotiation environment for heterogeneous agents. In: Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems. pp. 1397–1398 (2009)Google Scholar
  21. 21.
    Hindriks, K., Tykhonov, D.: Opponent modelling in automated multi-issue negotiation using bayesian learning. In: 7th International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 331–338 (2008)Google Scholar
  22. 22.
    Ikarashi, M., Fujita, K.: Compromising strategy using weighted counting in multi-times negotiations. In: Proceedings of the 3rd International Conference on Advanced Applied Informatics, pp. 453–458 (2014)Google Scholar
  23. 23.
    Jennings, N.R., Faratin, P., Lomuscio, A.R., Parsons, S., Wooldridge, M.J., Sierra, C.: Automated negotiation: prospects, methods and challenges. Group Decis. Negot. 10, 199–215 (2001)CrossRefGoogle Scholar
  24. 24.
    Kawaguchi, S., Fujita, K., Ito, T.: AgentK: Compromising strategy based on estimated maximum utility for automated negotiating agents. In: Ito, T., Zhang, M., Robu, V., Fatima, S., Matsuo, T. (eds.) New Trends in Agent-Based Complex Automated Negotiations. SCI, vol. 383, pp. 137–144. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-24696-8_8 CrossRefGoogle Scholar
  25. 25.
    van Krimpen, T., Looije, D., Hajizadeh, S.: HardHeaded. In: Ito, T., Zhang, M., Robu, V., Matsuo, T. (eds.) Complex Automated Negotiations: Theories, Models, and Software Competitions. SCI, vol. 435, pp. 223–227. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-30737-9_17 CrossRefGoogle Scholar
  26. 26.
    Luo, X., Jennings, N.R., Shadbolt, N., Leung, H.F., Lee, J.H.M.: A fuzzy constraint based model for bilateral, multi-issue negotiations in semi-competitive environments. Artif. Intell. 148(1), 53–102 (2003)CrossRefzbMATHGoogle Scholar
  27. 27.
    Ossowski, S., Sierra, C., Botti, V.: Agreement technologies: a computing perspective. In: Ossowski, S. (ed.) Agreement Technologies. LGTS, vol. 8, pp. 3–16. Springer, Dordrecht (2013). doi: 10.1007/978-94-007-5583-3_1 CrossRefGoogle Scholar
  28. 28.
    Sanchez-Anguix, V., Aydogan, R., Julian, V., Jonker, C.: Unanimously acceptable agreements for negotiation teams in unpredictable domains. Electron. Commer. Res. Appl. 13(4), 243–265 (2014)CrossRefGoogle Scholar
  29. 29.
    Sanchez-Anguix, V., Julian, V., Botti, V., García-Fornes, A.: Tasks for agent-based negotiation teams: analysis, review, and challenges. Eng. Appl. Artif. Intel. 26(10), 2480–2494 (2013)CrossRefzbMATHGoogle Scholar
  30. 30.
    Williams, C.R., Robu, V., Gerding, E.H., Jennings, N.R.: IAMhaggler2011: a gaussian process regression based negotiation agent. In: Ito, T., Zhang, M., Robu, V., Matsuo, T. (eds.) Complex Automated Negotiations: Theories, Models, and Software Competitions. SCI, vol. 435, pp. 209–212. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-30737-9_14 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Okan Tunalı
    • 1
  • Reyhan Aydoğan
    • 1
    • 2
  • Victor Sanchez-Anguix
    • 3
  1. 1.Department of Computer ScienceÖzyeğin UniversityIstanbulTurkey
  2. 2.Interactive Intelligence GroupDelft University of TechnologyDelftThe Netherlands
  3. 3.Coventry UniversityCoventryUK

Personalised recommendations