Abstract
An important aim in bilateral negotiations is to achieve a win-win solution for both parties; therefore, a critical aspect of a negotiating agent’s success is its ability to take the opponent’s preferences into account. Every year, new negotiation agents are introduced with better learning techniques to model the opponent. Our main goal in this work is to evaluate and compare the performance of a selection of state-of-the-art online opponent modeling techniques in negotiation, and to determine under which circumstances they are beneficial in a real-time, online negotiation setting. Towards this end, we provide an overview of the factors influencing the quality of a model and we analyze how the performance of opponent models depends on the negotiation setting. This results in better insight into the performance of opponent models, and allows us to pinpoint well-performing opponent modeling techniques that did not receive much previous attention in literature.
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References
Baarslag, T., Fujita, K., Gerding, E.H., Hindriks, K., Ito, T., Jennings, N.R., Jonker, C., Kraus, S., Lin, R., Robu, V., Williams, C.R.: Evaluating practical negotiating agents: Results and analysis of the 2011 international competition. Artificial Intelligence Journal (accepted)
Baarslag, T., Hindriks, K., Hendrikx, M., Dirkzwager, A., Jonker, C.: Decoupling negotiating agents to explore the space of negotiation strategies. In: Proceedings of the 5th International Workshop on Agent-based Complex Automated Negotiations, ACAN 2012 (2012)
Baarslag, T., Hindriks, K., Jonker, C.: A Tit for Tat Negotiation Strategy for Real-Time Bilateral Negotiations. In: Ito, T., Zhang, M., Robu, V., Matsuo, T. (eds.) Complex Automated Negotiations: Theories, Models, and Software Competitions. SCI, vol. 435, pp. 231–236. Springer, Heidelberg (2012)
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)
Buffett, S., Spencer, B.: A bayesian classifier for learning opponents’ preferences in multi-object automated negotiation. ECRA 6, 274–284 (2007)
Coehoorn, R., Jennings, N.: Learning an opponent’s preferences to make effective multi-issue negotiation trade-offs. In: Proceedings of the ICEC 2004, pp. 59–68. ACM (2004)
Faratin, P., Sierra, C., Jennings, N.: Negotiation decision functions for autonomous agents. Robotics and Autonomous Systems 24(3-4), 159–182 (1998)
van Galen Last, 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)
Hindriks, K., Jonker, C., Tykhonov, D.: Negotiation dynamics: Analysis, concession tactics, and outcomes. In: IEEE/WIC/ACM International Conference on Intelligent Agent Technology, pp. 427–433. IEEE Computer Society (2007)
Hindriks, K., Tykhonov, D.: Opponent modelling in automated multi-issue negotiation using bayesian learning. In: Proceedings of the 7th AAMAS 2008 (2008)
Hindriks, K.V., Tykhonov, D.: Towards a Quality Assessment Method for Learning Preference Profiles in Negotiation. In: Ketter, W., La Poutré, H., Sadeh, N., Shehory, O., Walsh, W. (eds.) AMEC 2008. LNBIP, vol. 44, pp. 46–59. Springer, Heidelberg (2010)
Kersten, G.E., Zhang, G.: Mining inspire data for the determinants of successful internet negotiations. Central European Journal of Operational Research (2003)
Klos, T., Somefun, K., La Poutré, H.: Automated interactive sales processes. IEEE Intelligent Systems 26, 54–61 (2010)
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. 225–230. Springer, Heidelberg (2012)
Lin, R., Kraus, S., Baarslag, T., Tykhonov, D., Hindriks, K., Jonker, C.M.: Genius: An integrated environment for supporting the design of generic automated negotiators. In: Computational Intelligence (2012)
Lin, R., Kraus, S., Wilkenfeld, J., Barry, J.: Negotiating with bounded rational agents in environments with incomplete information using an automated agent. Ai 172, 823–851 (2008)
Rubinstein, A.: Perfect equilibrium in a bargaining model. Econometrica: Journal of the Econometric Society 50, 97–109 (1982)
Dan Şerban, L., Silaghi, G.C., Litan, C.M.: AgentFSEGA: Time Constrained Reasoning Model for Bilateral Multi-issue Negotiations. In: Ito, T., Zhang, M., Robu, V., Fatima, S., Matsuo, T. (eds.) New Trends in Agent-Based Complex Automated Negotiations. SCI, vol. 383, pp. 159–165. Springer, Heidelberg (2012)
Thompson, L.: The Mind and heart of the negotiator, 3rd edn. Prentice Hall Press, Upper Saddle River (2000)
Williams, C.R., Robu, V., Gerding, E.H., Jennings, N.R.: IAMhaggler: A Negotiation Agent for Complex Environments. In: Ito, T., Zhang, M., Robu, V., Fatima, S., Matsuo, T. (eds.) New Trends in Agent-Based Complex Automated Negotiations. SCI, vol. 383, pp. 151–158. Springer, Heidelberg (2012)
Zeng, D., Sycara, K.: Bayesian learning in negotiation. International Journal of Human-Computers Studies 48(1), 125–141 (1998)
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Baarslag, T., Hendrikx, M., Hindriks, K., Jonker, C. (2012). Measuring the Performance of Online Opponent Models in Automated Bilateral Negotiation. In: Thielscher, M., Zhang, D. (eds) AI 2012: Advances in Artificial Intelligence. AI 2012. Lecture Notes in Computer Science(), vol 7691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35101-3_1
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DOI: https://doi.org/10.1007/978-3-642-35101-3_1
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