From Research to Practice: Automated Negotiations with People

Chapter
Part of the Cognitive Technologies book series (COGTECH)

Abstract

The development of proficient automated agents has flourished in recent years, yet making the agents interact with people has still received little attention. This is mainly due to the unpredictable nature of people and their negotiation behavior, though complexity and costs attached to experimentation with people, starting from the design and ending with the evaluation process, is also a factor. Even so, succeeding in designing proficient automated agents remains an important objective. In recent years, we have invested much effort in facilitating the design and evaluation of automated agents interacting with people, making it more accessible to researchers. We have created two distinct environments for bargaining agents, as well as proposing a novel approach for evaluating agents. These are key factors for making automated agents become a reality rather than remain theoretical.

Keywords

Utility Function Acceptance Threshold Automate Negotiation Negotiation Partner Negotiation Session 
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.

References

  1. 1.
    Bazerman, M.H., Neale, M.A.: Negotiator rationality and negotiator cognition: The interactive roles of prescriptive and descriptive research. In: Young, H.P. (ed.) Negotiation Analysis, pp. 109–130. The University of Michigan Press (1992)Google Scholar
  2. 2.
    Camerer, C.F.: In: Behavioral Game Theory. Experiments in Strategic Interaction, chap. 2. Princeton University Press (2003)Google Scholar
  3. 3.
    Davis, R., Smith, R.G.: Negotiation as a metaphor for distributed problem solving. Artificial Intelligence 20, 63–109 (1983)CrossRefGoogle Scholar
  4. 4.
    Erev, I., Roth, A.: Predicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibrium. American Economic Review 88(4), 848–881 (1998)Google Scholar
  5. 5.
    Gal, Y., Grosz, B., Kraus, S., Pfefer, A., Shieber, S.: Agent decision-making in open mixed networks. AIJ (accepted, 2010)Google Scholar
  6. 6.
    Gal, Y., Kraus, S., Gelfand, M., Blumberg, Y., Salmon, E.: An adaptive agent for negotiating with people in different cultures. ACM Transactions on Intelligent Systems and Technology (ACM TIST) (accepted, 2010)Google Scholar
  7. 7.
    Grosz, B., Kraus, S., Talman, S., Stossel, B.: The influence of social dependencies on decision-making: Initial investigations with a new game. In: AAMAS. pp. 782–789 (2004)Google Scholar
  8. 8.
    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
  9. 9.
    Leonard, T., Hsu, J.S.J.: Bayesian Methods – An Analysis for Statisticians and interdisciplinary Researchers. Cambridge University Press, Cambridge, UK (1999)MATHGoogle Scholar
  10. 10.
    Lin, R., Kraus, S., Oshrat, Y., Gal, Y.K.: Facilitating the evaluation of automated negotiators using peer designed agents. In: AAAI. pp. 817–822 (2010)Google Scholar
  11. 11.
    Lin, R., Kraus, S., Tykhonov, D., Hindriks, K., Jonker, C.M.: Supporting the design of general automated negotiators. In: ACAN (2009)Google Scholar
  12. 12.
    Lin, R., Kraus, S., Wilkenfeld, J., Barry, J.: Negotiating with bounded rational agents in environments with incomplete information using an automated agent. AIJ 172(6–7), 823–851 (2008)MathSciNetMATHGoogle Scholar
  13. 13.
    Luce, R.D.: Individual Choice Behavior: A Theoretical Analysis. John Wiley & Sons, NY (1959)MATHGoogle Scholar
  14. 14.
    McKelvey, R.D., Palfrey, T.R.: An experimental study of the centipede game. Econometrica 60(4), 803–836 (1992)MATHCrossRefGoogle 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)MATHCrossRefGoogle Scholar
  16. 16.
    Osborne, M.J., Rubinstein, A.: A Course In Game Theory. MIT Press, Cambridge MA (1994)MATHGoogle Scholar
  17. 17.
    Oshrat, Y., Lin, R., Kraus, S.: Facing the challenge of human-agent negotiations via effective general opponent modeling. In: AAMAS. pp. 377–384 (2009)Google Scholar
  18. 18.
    Selten, R., Abbink, K., Buchta, J., Sadrieh, A.: How to play (3 × 3)-games: A strategy method experiment. Games and Economic Behavior 45(1), 19–37 (2003)MathSciNetMATHCrossRefGoogle Scholar
  19. 19.
    Selten, R., Mitzkewitz, M., Uhlich, G.R.: Duopoly strategies programmed by experienced players. Econometrica 65(3), 517–556 (1997)MathSciNetMATHCrossRefGoogle Scholar
  20. 20.
    Tversky, A., Kahneman, D.: The framing of decisions and the psychology of choice. Science 211, 453–458 (1981)MathSciNetMATHCrossRefGoogle Scholar
  21. 21.
    Wand, M., Jones., M.: Kernel Smoothing. Chapman & Hall, London (1995)MATHGoogle Scholar

Copyright information

© 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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