Skip to main content
SpringerLink
Log in

Modeling agents based on aspiration adaptation theory

  • Published:
Autonomous Agents and Multi-Agent Systems Aims and scope Submit manuscript

Abstract

Creating agents that realistically simulate and interact with people is an important problem. In this paper we present strong empirical evidence that such agents should be based on bounded rationality, and specifically on key elements from Aspiration Adaptation Theory (AAT). First, we analyzed the strategies people described they would use to solve two relatively basic optimization problems involving one and two parameters. Second, we studied the agents a different group of people wrote to solve these same problems. We then studied two realistic negotiation problems involving five and six parameters. Again, first we studied the negotiation strategies people used when interacting with other people. Then we studied two state of the art automated negotiation agents and negotiation sessions between these agents and people. We found that in both the optimizing and negotiation problems the overwhelming majority of automated agents and people used key elements from AAT, even when optimal solutions, machine learning techniques for solving multiple parameters, or bounded techniques other than AAT could have been implemented. We discuss the implications of our findings including suggestions for designing more effective agents for game and simulation environments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Chalamish, M., Sarne D., & Kraus, S. (2008). Programming agents as a means of capturing self-strategy. In AAMAS ’08, pp. 1161–1168.

  2. Gigerenzer G., Goldstein D. G. (1996) Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review 103(4): 650–669

    Article  Google Scholar 

  3. Hopmann P. T. (1996) The negotiation process and the resolution of international conflicts. University of South Carolina Press, Columbia, SC

    Google Scholar 

  4. Kaelbling L. P., Littman M. L., Cassandra A. R. (1998) Planning and acting in partially observable stochastic domains. Artificial Intelligence 101(1–2): 99–134

    Article  MATH  MathSciNet  Google Scholar 

  5. Kahneman D., Tversky A. (1979) Prospect theory: An analysis of decision under risk. Econometrica 47: 263–291

    Article  MATH  Google Scholar 

  6. Kenny, P., Hartholt, A., Jonathan, G., Swartout, W., Traum, D., Marsella, S., & Piepol, D. (2007). Building interactive virtual humans for training environments. In Proceedings of interservice/industry training, simulation and education conference (I/ITSEC).

  7. Kersten G. E., Noronha S. J. (1999) www-based negotiation support: design, implementation, and use. Decision Support Systems 25(2): 135–154

    Article  Google Scholar 

  8. Keser C., Gardner R. (1999) Strategic behavior of experienced subjects in a common pool resource game. International Journal of Game Theory 28(2): 241–252

    Article  MATH  Google Scholar 

  9. Kraus S., Hoz-Weiss P., Wilkenfeld J., Andersen D. R., Pate A. (2008) Resolving crises through automated bilateral negotiations. Artificial Intelligence 172(1): 1–18

    Article  MATH  MathSciNet  Google Scholar 

  10. Lin R., Kraus S. (2010) Can automated agents proficiently negotiate with humans? Communications of the ACM, 53(1): 78–88

    Article  Google Scholar 

  11. Lin R., Kraus S., Wilkenfeld J., Barry J. (2008) Negotiating with bounded rational agents in environments with incomplete information using an automated agent. Artificial Intelligence 172(6–7): 823–851

    Article  MATH  MathSciNet  Google Scholar 

  12. Lin, R., Oshrat, Y., & Kraus, S. (2009). Investigating the benefits of automated negotiations in enhancing people’s negotiation skills. In Proceedings of the 8th international conference on autonomous agents and multiagent systems (AAMAS), pp. 345–352.

  13. Maes P. (1995) Artificial life meets entertainment: Lifelike autonomous agents. Communications of the ACM 38(11): 108–114

    Article  Google Scholar 

  14. Manisterski, E., Lin, R., & Kraus, S. (2008). Understanding how people design trading agents over time. In AAMAS ’08, pp. 1593–1596.

  15. Murakami, Y., Minami, K., Kawasoe, T., & Ishida, T. (2002). Multi-agent simulation for crisis management. In IEEE workshop on knowledge media networking (KMN’602).

  16. Murakami, Y., Sugimoto, Y., Ishida, T. (2005). Modeling human behavior for virtual training systems. In: AAAI, pp. 127–132.

  17. Osborne M. J., Rubinstein A. (1994) A course in game theory. MIT Press, Cambridge

    MATH  Google Scholar 

  18. Oshrat, Y., Lin, R., & Kraus, S. (2009). Facing the challenge of human-agent negotiations via effective general opponent modeling. In Proceedings of the 8th international conference on autonomous agents and multiagent systems (AAMAS), pp. 377–384.

  19. Pynadath D. V., Tambe M. (2002) The communicative multiagent team decision problem: Analyzing teamwork theories and models. Journal of Artificial Intelligence Research 16: 389–423

    MATH  MathSciNet  Google Scholar 

  20. Russell S. J., Norvig P. (2003) Artificial intelligence: A modern approach (2nd ed.). Prentice Hall, Upper Saddle River

    Google Scholar 

  21. Sauermann H., Selten R. (1962) Anspruchsanpassungstheorie der unternehmung. Zeitschrift fr die Gesamte Staatswissenschaft 118: 577–597

    Google Scholar 

  22. Selten R. (1998) Aspiration adaptation theory. Journal of Mathematical Psychology 42: 191–214

    Article  MATH  Google Scholar 

  23. Selten R., Abbink K., Buchta J., Sadrieh A. (2003) How to play (3  ×  3)-games: A strategy method experiment. Games and Economic Behavior 45(1): 19–37

    Article  MATH  MathSciNet  Google Scholar 

  24. Selten R., Mitzkewitz M., Uhlich G. R. (1997) Duopoly strategies programmed by experienced players. Econometrica 65(3): 517–556

    Article  MATH  MathSciNet  Google Scholar 

  25. Selten, R., Pittnauer, S., & Hohnisch, M. (2008) Experimental results on the process of goal formation and aspiration adaptation. In Technical report—Bonn Laboratory for Experimental Economics.

  26. Simon H. A. (1957) Models of man. Wiley, New York

    MATH  Google Scholar 

  27. Tennenholtz, M. (1996). On stable social laws and qualitative equilibrium for risk-averse agents. In Proceedings of the 5th international conference on principles of knowledge representation and reasoning (KR-96), pp 553–561.

  28. Thiessen E. M., Loucks D. P., Stedinger J. R. (1998) Computer-assisted negotiations of water resources conflicts. Group Decision and Negotiation 7(2): 109–129

    Article  Google Scholar 

  29. Thrun, S. (2002). Robotic mapping: A survey. In Exploring Artificial Intelligence in the New Millenium. San Fancisco: Morgan Kaufmann.

  30. Von Neumann J., Morgenstern O. (1944) Theory of games and economic behavior. Princeton University Press, Princeton

    MATH  Google Scholar 

  31. Weitzman M. L. (1979) Optimal search for the best alternative. Econometrica 47(3): 641–654

    Article  MATH  MathSciNet  Google Scholar 

  32. Wilkenfeld J., Young K., Quinn D., Asal V. (2005) Mediating international crises. Routledge, London

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering of the Jerusalem College of Technology, 91160, Jerusalem, Israel

    Avi Rosenfeld

  2. Department of Computer Science of Bar-Ilan University, 52900, Ramat-Gan, Israel

    Sarit Kraus

Authors
  1. Avi Rosenfeld
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sarit Kraus
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Avi Rosenfeld.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rosenfeld, A., Kraus, S. Modeling agents based on aspiration adaptation theory. Auton Agent Multi-Agent Syst 24, 221–254 (2012). https://doi.org/10.1007/s10458-010-9158-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10458-010-9158-x

Keywords

Search

Navigation