Automated Agents that Proficiently Negotiate with People: Can We Keep People out of the Evaluation Loop

  • Raz Lin
  • Yinon Oshrat
  • Sarit Kraus
Part of the Studies in Computational Intelligence book series (SCI, volume 383)


Research on automated negotiators has flourished in recent years. Among the important issues considered is how these automated negotiators can proficiently negotiate with people. To validate this, many experimentations with people are required. Nonetheless, conducting experiments with people is timely and costly, making the evaluation of these automated negotiators a very difficult process. Moreover, each revision of the agent’s strategies requires to gather an additional set of people for the experiments. In this paper we investigate the use of Peer Designed Agents (PDAs) – computer agents developed by human subjects – as a method for evaluating automated negotiators. We have examined the negotiation results and its dynamics in extensive simulations with more than 300 human negotiators and more than 50 PDAs in two distinct negotiation environments. Results show that computer agents perform better than PDAs in the same negotiation contexts in which they perform better than people, and that on average, they exhibit the same measure of generosity towards their negotiation partners. Thus, we found that using the method of peer designed negotiators embodies the promise of relieving some of the need for people when evaluating automated negotiators.


Multiagent System Strategy Method Bilateral Negotiation Automate Negotiation Cooperation Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Byde, A., Yearworth, M., Chen, K.-Y., Bartolini, C.: AutONA: A system for automated multiple 1-1 negotiation. In: Proceedings of the 2003 IEEE International Conference on Electronic Commerc (CEC), pp. 59–67 (2003)Google Scholar
  2. 2.
    Chalamish, M., Sarne, D., Kraus, S.: Programming agents as a means of capturing self-strategy. In: Proceedings of the Seventh International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 1161–1168 (2008)Google Scholar
  3. 3.
    Fleming, M., Olsen, D., Stathes, H., Boteler, L., Grossberg, P., Pfeifer, J., Schiro, S., Banning, J., Skochelak, S.: Virtual reality skills training for health care professionals in alcohol screening and brief intervention. Journal of the American Board of Family Medicine 22(4), 387–398 (2009)CrossRefGoogle Scholar
  4. 4.
    Gal, Y., Pfeffer, A., Marzo, F., Grosz, B.J.: Learning social preferences in games. In: Proceedinge of the Nineteenth AAAI Conference on Artificial Intelligence, pp. 226–231 (2004)Google Scholar
  5. 5.
    Grosz, B., Kraus, S., Talman, S., Stossel, B.: The influence of social dependencies on decision-making: Initial investigations with a new game. In: Proceedings of the Third International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 782–789 (2004)Google Scholar
  6. 6.
    Hindriks, K., Jonker, C., Tykhonov, D.: Analysis of negotiation dynamics. In: Klusch, M., Hindriks, K.V., Papazoglou, M.P., Sterling, L. (eds.) CIA 2007. LNCS (LNAI), vol. 4676, pp. 27–35. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Hindriks, K., Jonker, C., Tykhonov, D.: Negotiation dynamics: Analysis, concession tactics, and outcomes. In: Proceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT), pp. 427–433 (2007)Google Scholar
  8. 8.
    Jonker, C.M., Robu, V., Treur, J.: An agent architecture for multi-attribute negotiation using incomplete preference information. Autonomous Agents and Multi-Agent Systems 15(2), 221–252 (2007)CrossRefGoogle Scholar
  9. 9.
    Katz, R., Kraus, S.: Efficient agents for cliff edge environments with a large set of decision options. In: Proceedings of the Fifth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 697–704 (2006)Google Scholar
  10. 10.
    Kraus, S., Hoz-Weiss, P., Wilkenfeld, J., Andersen, D.R., Pate, A.: Resolving crises through automated bilateral negotiations. Artificial Intelligence 172(1), 1–18 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Kraus, S., Lehmann, D.: Designing and building a negotiating automated agent. Computational Intelligence 11(1), 132–171 (1995)CrossRefGoogle Scholar
  12. 12.
    Lax, D.A., Sebenius, J.K.: Thinking coalitionally: party arithmetic, process opportunism, and strategic sequencing. In: Young, H.P. (ed.) Negotiation Analysis, pp. 153–193. The University of Michigan Press (1992)Google Scholar
  13. 13.
    Lin, R., Kraus, S.: Can automated agents proficiently negotiate with humans? Communications of the ACM 53(1), 78–88 (2010)CrossRefGoogle Scholar
  14. 14.
    Lin, R., Kraus, S., Wilkenfeld, J., Barry, J.: Negotiating with bounded rational agents in environments with incomplete information using an automated agent. Artificial Intelligence 172(6-7), 823–851 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Offerman, T., Potters, J., Verbon, H.A.A.: Cooperation in an overlapping generations experiment. Games and Economic Behavior 36(2), 264–275 (2001)zbMATHCrossRefGoogle Scholar
  16. 16.
    Olsen, D.E.: Interview and interrogation training using a computer-simulated subject. In: Interservice/Industry Training, Simulation and Education Conference (1997)Google Scholar
  17. 17.
    Osborne, M.J., Rubinstein, A.: A Course In Game Theory. MIT Press, Cambridge (1994)zbMATHGoogle Scholar
  18. 18.
    Oshrat, Y., Lin, R., Kraus, S.: Facing the challenge of human-agent negotiations via effective general opponent modeling. In: Proceedings of the Eighth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 377–384 (2009)Google Scholar
  19. 19.
    Rosenfeld, A., Kraus, S.: Modeling agents through bounded rationality theories. In: Proceedings of the Tewenty-First International Joint Conference on Artificial Intelligence (IJCAI), pp. 264–271 (2009)Google Scholar
  20. 20.
    Selten, R., Abbink, K., Buchta, J., Sadrieh, A.: How to play (3x3)-games: A strategy method experiment. Games and Economic Behavior 45(1), 19–37 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  21. 21.
    Selten, R., Mitzkewitz, M., Uhlich, G.R.: Duopoly strategies programmed by experienced players. Econometrica 65(3), 517–556 (1997)MathSciNetzbMATHCrossRefGoogle Scholar
  22. 22.
    TAC Team: A trading agent competition. IEEE Internet Computing 5(2), 43–51 (2001)CrossRefGoogle Scholar
  23. 23.
    Talman, S., Hadad, M., Gal, Y., Kraus, S.: Adapting to agents’ personalities in negotiation. In: Proceedings of the Fourth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 383–389 (2005)Google Scholar
  24. 24.
    Traum, D.R., Marsella, S.C., Gratch, J., Lee, J., Hartholt, A.: Multi-party, Multi-Issue, Multi-Strategy Negotiation for Multi-Modal Virtual Agents. In: Prendinger, H., Lester, J.C., Ishizuka, M. (eds.) IVA 2008. LNCS (LNAI), vol. 5208, pp. 117–130. Springer, Heidelberg (2008)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer ScienceBar-Ilan UniversityRamat-GanIsrael
  2. 2.Institute for Advanced Computer StudiesUniversity of MarylandCollege ParkUSA

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