Advertisement

Agents in Electronic Commerce: Component Technologies for Automated Negotiation and Coalition Formation

  • Tuomas Sandholm
Article

Abstract

Automated negotiation and coalition formation among self-interested agents are playing an increasingly important role in electronic commerce. Such agents cannot be coordinated by externally imposing their strategies. Instead the interaction protocols have to be designed so that each agent is motivated to follow the strategy that the protocol designer wants it to follow. This paper reviews six component technologies that we have developed for making such interactions less manipulable and more efficient in terms of the computational processes and the outcomes: 1. OCSM-contracts in marginal cost based contracting, 2. leveled commitment contracts, 3. anytime coalition structure generation with worst case guarantees, 4. trading off computation cost against optimization quality within each coalition, 5. distributing search among insincere agents, and 6. unenforced contract execution. Each of these technologies represents a different way of battling self-interest and combinatorial complexity simultaneously. This is a key battle when multi-agent systems move into large-scale open settings.

multiagent systems electronic commerce negotiation contracting coalition formation game theory anytime algorithm resource-bounded reasoning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    M. R. Andersson and T. W. Sandholm, ''Leveled commitment contracting among myopic individually rational agents,'' in Proceedings of the Third International Conference on MultiAgent Systems (ICMAS), Paris, France, 1998, pp. 26–33.Google Scholar
  2. 2.
    M. R. Andersson and T. W. Sandholm, ''Leveled commitment contracts with myopic and strategic agents,'' in Proceedings of the National Conference on Artificial Intelligence (AAAI), Madison, WI, 1998, pp. 38–45.Google Scholar
  3. 3.
    M. R. Andersson and T. W. Sandholm, ''Time-quality tradeoffs in reallocative negotiation with combinatorial contract types,'' in Proceedings of the National Conference on Artificial Intelligence (AAAI), Orlando, FL, 1999, pp. 3–10.Google Scholar
  4. 4.
    M. Boddy and T. Dean, ''Deliberation scheduling for problem solving in time-constrained environments,'' Artificial Intelligence, vol. 67, pp. 245–285, 1994.CrossRefGoogle Scholar
  5. 5.
    A. H. Bond and L. Gasser, Readings in Distributed Artificial Intelligence, Morgan Kaufmann Publishers: San Mateo, CA, 1988.Google Scholar
  6. 6.
    J. Q. Cheng and M. P. Wellman, The WALRAS algorithm: ''A convergent distributed implementation of general equilibrium outcomes,'' Computational Economics, vol. 12, pp. 1–24, 1998.Google Scholar
  7. 7.
    E. Durfee, V. Lesser, and D. Corkill, ''Cooperative distributed problem solving,'' in The Handbook of Artificial Intelligence, A. Barr, P. Cohen, and E. Feigenbaum (Eds.), Addison Wesley 1989, vol. IV, pp. 83–147.Google Scholar
  8. 8.
    E. Ephrati and J. S. Rosenschein, ''The Clarke tax as a consensus mechanism among automated agents,'' in Proceedings of the National Conference on Artificial Intelligence (AAAI), Anaheim, CA, 1991, pp. 173–178.Google Scholar
  9. 9.
    C. Gu and T. Ishida, ''Analyzing the social behavior of contract net protocol,'' in Agents Breaking Away; MAAMAW'96, Lecture Notes in Artificial Intelligence 1038, W. Van de Velde and J. W. Perram, (Eds.), Springer-Verlag, 1996, pp. 116–127.Google Scholar
  10. 10.
    N. Jennings, P. Faratin, T. Norman, P. O'Brien, B. Odgers, and J. Alty, ''Implementing a business process management system using ADEPT: A real-world case study,'' International Journal of Applied Artificial Intelligence, 1999, to appear.Google Scholar
  11. 11.
    J. P. Kahan and A. Rapoport, Theories of Coalition Formation, Lawrence Erlbaum Associates Publishers, 1984.Google Scholar
  12. 12.
    S. Ketchpel, Forming coalitions in the face of uncertain rewards, in Proceedings of the National Conference on Artificial Intelligence (AAAI), Seattle, WA, July 1994, pp. 414–419.Google Scholar
  13. 13.
    S. Kraus, J. Wilkenfeld, and G. Zlotkin, ''Multiagent negotiation under time constraints,'' Artificial Intelligence, vol. 75, pp. 297–345, 1995.CrossRefGoogle Scholar
  14. 14.
    D. M. Kreps, A Course in Microeconomic Theory, Princeton University Press, 1990.Google Scholar
  15. 15.
    K. S. Larson and T. W. Sandholm, ''Anytime coalition structure generation: An average case study,'' in Proceedings of the Third International Conference on Autonomous Agents (AGENTS), Seattle, WA, May 1999, pp. 40–47.Google Scholar
  16. 16.
    A. Mas-Colell, M. Whinston, and J. R. Green, Microeconomic Theory, Oxford University Press, 1995.Google Scholar
  17. 17.
    H. Raiffa, The Art and Science of Negotiation, Harvard Univ. Press: Cambridge, MA, 1982.Google Scholar
  18. 18.
    J. S. Rosenschein and G. Zlotkin, Rules of Encounter: Designing Conventions for Automated Negotiation among Computers, MIT Press, 1994.Google Scholar
  19. 19.
    S. Russell and D. Subramanian, ''Provably bounded-optimal agents,'' Journal of Artificial Intelligence Research, vol. 1, pp. 1–36, 1995.Google Scholar
  20. 20.
    T. W. Sandholm, ''A strategy for decreasing the total transportation costs among area-distributed transportation centers,'' in Nordic Operations Analysis in Cooperation (NOAS): OR in Business, Turku School of Economics: Finland, 1991.Google Scholar
  21. 21.
    T. W. Sandholm, ''Automatic cooperation of area-distributed dispatch centers in vehicle routing,'' in International Conference on Artificial Intelligence Applications in Transportation Engineering, San Buenaventura, CA, 1992, pp. 449–467.Google Scholar
  22. 22.
    T. W. Sandholm, ''An implementation of the contract net protocol based on marginal cost calculations,'' in Proceedings of the National Conference on Artificial Intelligence (AAAI), Washington, D.C., July 1993, pp. 256–262.Google Scholar
  23. 23.
    T. W. Sandholm, ''Limitations of the Vickrey auction in computational multiagent systems,'' in Proceedings of the Second International Conference on MultiAgent Systems (ICMAS), Keihanna Plaza, Kyoto, Japan, December 1996, pp. 299–306.Google Scholar
  24. 24.
    T. W. Sandholm, ''Negotiation among Self-Interested Computationally Limited Agents,'' Ph.D. thesis, University of Massachusetts, Amherst, 1996. Available at http://www.cs.wustl.edu/∼sand-holm/dissertation.ps.Google Scholar
  25. 25.
    T. W. Sandholm, ''Contract types for satisficing task allocation: I theoretical results,'' in AAAI Spring Symposium Series: Satisficing Models, Stanford University, CA, March 1998, pp. 68–75.Google Scholar
  26. 26.
    T. W. Sandholm, ''An algorithm for optimal winner determination in combinatorial auctions,'' in Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, Sweden, 1999, pp. 542–547. Extended version: Washington University, Department of Computer Science technical report WUCS-99–01, January.Google Scholar
  27. 27.
    T. W. Sandholm, K. S. Larson, M. R. Andersson, O. Shehory, and F. Tohmé, ''Coalition structure generation with worst case guarantees,'' Artificial Intelligence, 1999, to appear. Early version appeared at the National Conference on Artificial Intelligence (AAAI), 1998, pp. 46–53.Google Scholar
  28. 28.
    T. W. Sandholm and V. R. Lesser, ''Equilibrium analysis of the possibilities of unenforced exchange in multiagent systems,'' in Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI), Montreal, Canada, August 1995, pp. 694–701.Google Scholar
  29. 29.
    T. W. Sandholm and V. R. Lesser, ''Issues in automated negotiation and electronic commerce: Extending the contract net framework,'' in Proceedings of the First International Conference on MultiAgent Systems (ICMAS), San Francisco, CA, June 1995, pp. 328–335. Reprinted in Readings in Agents, Huhns and Singh (Eds.), 1997, pp. 66–73.Google Scholar
  30. 30.
    T. W. Sandholm and V. R. Lesser, ''Advantages of a leveled commitment contracting protocol,'' in Proceedings of the National Conference on Artificial Intelligence (AAAI), Portland, OR, August 1996, pp. 126–133.Google Scholar
  31. 31.
    T. W. Sandholm and V. R. Lesser, ''Coalitions among computationally bounded agents,'' Artificial Intelligence, 94(1), pp. 99–137, 1997. Special issue on Economic Principles of Multiagent Systems. Early version appeared at the International Joint Conference on Artificial Intelligence, pp. 662–669, 1995.Google Scholar
  32. 32.
    T. W. Sandholm, S. Sikka, and S. Norden, ''Algorithms for optimizing leveled commitment contracts,'' in Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, Sweden, 1999, pp. 535–540. Extended version: Washington University, Department of Computer Science technical report WUCS-99–02.Google Scholar
  33. 33.
    T. W. Sandholm and F. Ygge, ''On the gains and losses of speculation in equilibrium markets,'' in Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI), Nagoya, Japan, August 1997, pp. 632–638.Google Scholar
  34. 34.
    S. Sen, ''Tradeoffs in Contract-Based Distributed Scheduling,'' Ph.D. thesis, Univ. of Michigan, 1993.Google Scholar
  35. 35.
    S. Sen and E. Durfee, ''The role of commitment in cooperative negotiation,'' International Journal on Intelligent Cooperative Information Systems, vol. 3(1), pp. 67–81, 1994.Google Scholar
  36. 36.
    O. Shehory and S. Kraus, ''Task allocation via coalition formation among autonomous agents,'' in Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence IJCAI, Montreal, Canada, August 1995, pp. 655–661.Google Scholar
  37. 37.
    O. Shehory and S. Kraus, ''A kernel-oriented model for coalition-formation in general environments: Implemetation and results,'' in Proceedings of the National Conference on Artificial Intelligence (AAAI), Portland, OR, August 1996, pp. 134–140.Google Scholar
  38. 38.
    R. G. Smith, ''The contract net protocol: High-level communication and control in a distributed problem solver,'' IEEE Transactions on Computers, vol. C-29(12), pp. 1104–1113, December 1980.Google Scholar
  39. 39.
    K. Sycara, ''Multiagent compromise via negotiation,'' in Distributed Artificial Intelligence, Volume II, M. Huhns and L. Gasser (Eds.), Pittman Publishing Ltd and Morgan Kaufmann, 1989.Google Scholar
  40. 40.
    F. Tohmé and T. W. Sandholm, ''Coalition formation processes with belief revision among bounded ´ rational, self-interested agents,'' Journal of Logic and Computation, vol. 9(97–63), pp. 1–23, 1999. An early version appeared in the IJCAI-97 Workshop on Social Interaction and Communityware, Nagoya, Japan, pp. 43–51.Google Scholar
  41. 41.
    M. Wellman, ''A market-oriented programming environment and its application to distributed multicommodity flow problems, Journal of Artificial Intelligence Research, vol. 1, pp. 1–23, 1993.Google Scholar
  42. 42.
    S. Zilberstein and S. Russell, ''Optimal composition of real-time systems,'' Artificial Intelligence, vol. 82(1–2), pp. 181–213, 1996.Google Scholar
  43. 43.
    G. Zlotkin and J. S. Rosenschein, ''Coalition, cryptography and stability: Mechanisms for coalition formation in task oriented domains,'' in Proceedings of the National Conference on Artificial Intelligence (AAAI), Seattle, WA, July 1994, pp. 432–437.Google Scholar

Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Tuomas Sandholm
    • 1
  1. 1.Department of Computer ScienceWashington UniversitySt. Louis

Personalised recommendations