Abstract
Decoupling agents as we have done in Chap. 3 helps us to focus on the individual components of a negotiating agent’s design. One principal component of a negotiating agent’s strategy 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’s preferences. Our main goal in this chapter 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 a class of simple and surprisingly effective opponent modeling techniques that did not receive much previous attention in literature.
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References
Coehoorn RM, Jennings NR (2004) Learning an opponent’s preferences to make effective multi-issue negotiation trade-offs. In: Proceedings of the 6th international conference on Electronic commerce, ICEC ’04. ACM, New York, pp 59–68
Hindriks KV, Tykhonov D (2008) Opponent modelling in automated multi-issue negotiation using bayesian learning. In: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems, AAMAS ’08. International foundation for autonomous agents and multiagent systems, vol 1. Richland, SC, pp 331–338
Dajun Z, Sycara KP (1998) Bayesian learning in negotiation. Int J Hum Comput Stud 48(1):125–141
van Galen Last N (2012) 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. Studies in computational intelligence. Springer, Berlin, pp 167–174
van Krimpen T, Looije D, Hajizadeh S (2013) Hardheaded. In: Ito T, Zhang M, Robu V, Matsuo T (eds) Complex automated negotiations: theories, models, and software competitions. Studies in computational intelligence, vol 435. Springer, Berlin, pp 223–227
Baarslag T, Hindriks KV, Jonker CM (2013) 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. Studies in computational intelligence, vol 435. Springer, Berlin, pp 229–233
Williams CR, Robu V, Gerding EH, Jennings NR (2012) 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. Studies in Computational Intelligence. Springer, Berlin, pp 151–158
Faratin P, Sierra C, Jennings NR (1998) Negotiation decision functions for autonomous agents. Robot Auton Syst 24(3–4):159–182
Baarslag T, Katsuhide F, Gerding Enrico H, Hindriks Koen V, Takayuki I, Jennings Nicholas R, Jonker Catholijn M, Sarit K, Raz L, Valentin R, Williams Colin R (2013) Evaluating practical negotiating agents: results and analysis of the 2011 international competition. Artif Intell 198:73–103
Kersten Gregory E, Grant Z (2003) Mining inspire data for the determinants of successful internet negotiations. Central Eur J Oper Res 11(3):297–316
Leigh T (2000) The Mind and heart of the negotiator, 3rd edn. Prentice Hall Press, Upper Saddle River
Baarslag T, Hindriks KV, Jonker CM, Kraus S, Lin R (2012) 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. Studies in computational intelligence, vol 383. Springer, Berlin, pp 113–135
Klos Tomas B, Koye S, La Poutré JA (2011) Automated interactive sales processes. IEEE Intell Syst 26(4):54–61
Baarslag T, Hindriks KV, Hendrikx MJC, Dirkzwager ASY, Jonker CM (2012) Decoupling negotiating agents to explore the space of negotiation strategies. In: Proceedings of the fifth international workshop on agent-based complex automated negotiations (ACAN 2012)
Hindriks KV, Tykhonov D (2010) Towards a quality assessment method for learning preference profiles in negotiation. In: Ketter W, La Poutré Johannes A, Sadeh N, Shehory O, Walsh W (eds) Agent-mediated electronic commerce and trading agent design and analysis. Lecture notes in business information processing, vol 44. Springer, Berlin, pp 46–59
Şerban LD, Silaghi GC, Litan CM (2012) 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. Series of studies in computational intelligence. Springer, Berlin, pp 159–165
Buffett S, Spencer B (2007) A bayesian classifier for learning opponents’ preferences in multi-object automated negotiation. Electron Commer Res Appl 6(3):274–284
Lin R, Kraus S, Wilkenfeld J, Barry J (2008) Negotiating with bounded rational agents in environments with incomplete information using an automated agent. Artif Intell 172(6–7):823–851
Baarslag T, Hendrikx MJC, Hindriks KV, Jonker CM (2012) Measuring the performance of online opponent models in automated bilateral negotiation. In: Thielscher M, Zhang D (eds) AI 2012: Advances in artificial intelligence, vol 7691., Lecture notes in computer science. Springer, Berlin, Heidelberg, pp 1–14
This chapter is based on the following publications: [19]
Tim Baarslag, Mark J.C. Hendrikx, Koen V. Hindriks, and Catholijn M. Jonker. Measuring the performance of online opponent models in automated bilateral negotiation. In Michael Thielscher and Dongmo Zhang, editors, AI 2012: Advances in Artificial Intelligence, volume 7691 of Lecture Notes in Computer Science, pages 1–14. Springer Berlin Heidelberg, 2012
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Baarslag, T. (2016). Measuring the Performance of Online Opponent Models. In: Exploring the Strategy Space of Negotiating Agents. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-28243-5_6
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DOI: https://doi.org/10.1007/978-3-319-28243-5_6
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