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Auction Equilibrium Strategies for Task Allocation in Uncertain Environments

  • David Sarne
  • Meirav Hadad
  • Sarit Kraus
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3191)

Abstract

In this paper we address a model of self interested information agents competing to perform tasks. The agents are situated in an uncertain environment while different tasks dynamically arrive from a central manager. The agents differ in their capabilities to perform a task under different world states. Previous models concerning cooperative agents aiming for a joint goal are not applicable in such environments, since self interested agents have a motivation to deviate from the joint allocation strategy, in order to increase their own benefits. Given the allocation protocol set by the central manager, a stable solution, is a set of strategies, derived from an equilibrium where no agent can benefit from changing its strategy given the other agents’ strategies. Specifically we focus on a protocol in which, upon arrival of a new task, the central manager starts a reverse auction among the agents, and the agent who bids the lowest cost wins. We introduce the model, formulate its equations and suggest equilibrium strategies for the agents. By identifying specific characteristics of the equilibria, we manage to suggest an efficient algorithm for enhancing the agents’ calculation of the equilibrium strategies. A comparison with the central allocation mechanism, and the effect of environmental settings on the perceived equilibrium are given using several sample environments.

Keywords

Multiagent System Equilibrium Strategy Task Allocation Central Manager World State 
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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References

  1. 1.
    Altman, E., Hassin, R.: Non-threshold equilibrium for customers joining an m/g/1 queue. In: Proceedings of 10th International Symposium on Dynamic Game and Applications, Saint-Petersburg, Russia (July 2002)Google Scholar
  2. 2.
    Boutilier, C., Goldszmidt, M., Sabata, B.: Sequential auctions for the allocation of resources with complementarities. In: IJCAI 1999, pp. 527–523 (1999)Google Scholar
  3. 3.
    Brandt, F., Brauer, W., Weiss, G.: Task assignment in multiagent systems based on vickrey-type auctioning and leveled commitment contracting. In: Klusch, M., Kerschberg, L. (eds.) CIA 2000. LNCS (LNAI), vol. 1860, pp. 95–106. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  4. 4.
    Byde, A., Preist, C., Jennings, N.: Decision procedures for multiple auctions. In: AAMAS 2002 (2002)Google Scholar
  5. 5.
    Dias, B., Stentz, A.: Traderbots: A market-based approach for resource, role, and task allocation in multirobot coordination, Technical Report CMU-RI -TR- 03-19, Robotics Institute, CMU, PA (2003)Google Scholar
  6. 6.
    McMillan, J., Rothschild, M.: Search. In: Aumann, R.J., Hart, A.S. (eds.) Handbook of Game Theory with Economic Applications, pp. 905–927 (1994)Google Scholar
  7. 7.
    Sandholm, T., Lesser, V.: Issues in automated negotiation and electronic commerce: Extending the contract net framework. In: ICMAS 1995, pp. 328–335. MIT Press, Cambridge (1995)Google Scholar
  8. 8.
    Shehory, O.: Optimal bidding in multiple concurrent auctions. International Journal of Cooperative Information Systems 11(3-4), 315–327 (2002)CrossRefGoogle Scholar
  9. 9.
    Smith, R.G.: The contract net protocol: High-level communication and control in a distributed problem solver. IEEE Transactions on Computers 29(12), 1104–1113 (1980)CrossRefGoogle Scholar
  10. 10.
    Smith, T., Sandholm, T., Simmons, R.: Constructing and clearing combinatorial exchanges using preference elicitation, 2002. In: AAAI workshop on Preferences in AI and CP: Symbolic Approaches (2002)Google Scholar
  11. 11.
    Walsh, W., Wellman, M.: Efficiency and equilibrium in task allocation economies with hierarchical dependencies. In: IJCAI 1999, pp. 520–526 (1999)Google Scholar
  12. 12.
    Wellman, M., Walsh, W.: Auction protocols for decentralized scheduling. Games and Economic Behavior 35, 271–303 (2001)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • David Sarne
    • 1
  • Meirav Hadad
    • 2
  • Sarit Kraus
    • 1
    • 3
  1. 1.Department of Computer ScienceBar-Ilan UniversityRamat-GanIsrael
  2. 2.Caesarea Rothschild Institute University of Haifa Mount CarmelHaifaIsrael
  3. 3.Institute for Advanced Computer StudiesUniversity of MarylandCollege ParkUSA

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