A Combinatorial Auction Negotiation Protocol for Time-Restricted Group Decisions

  • Fabian Lang
  • Andreas Fink
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6943)


This paper focuses on multi-agent contract formation by automated negotiation. Commonly individuals are not willing to share information or cooperate and negotiation protocols may give way to unwanted strategic behavior. Socially beneficial contract agreements require a lot of negotiation time. Furthermore, possible interdependencies of contract items lead to complex contract spaces which restrain contract agreements. Therefore, we propose a novel negotiation protocol applying combinatorial auctions for contract formation which consider interdependencies and yield a rapid decision rights allocation. Additionally, this market-based approach utilizes Vickrey-Clarkes-Groves-mechanisms which may lead to truthful preference uncovering and information sharing through bids. However, combinatorial auctions have a computational drawback: winner determination is \(\mathcal{NP}\)-hard. In simulation experiments, two approximation algorithms as well as an optimal computation are tested in comparison with an established negotiation protocol. The results show that our protocol yields an effective solution and requires very short run time.


Social Welfare Greedy Algorithm Multiagent System Greedy Randomize Adaptive Search Procedure Incentive Compatibility 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Buer, T., Pankratz, G.: GRASP with Hybrid Path Relinking for Bi-Objective Winner Determination in Combinatorial Transportation Auctions. BuR - Business Research 3(2), 192–213 (2010)CrossRefGoogle Scholar
  2. 2.
    Conitzer, V., Sandholm, T.: Self-interested Automated Mechanism Design and Implications for Optimal Combinatorial Auctions. In: Proceedings of the 5th ACM Conference on Electronic Commerce, EC 2004 (2004)Google Scholar
  3. 3.
    Delorme, X., Gandibleux, X., Rodriguez, J.: GRASP for Set Packing Problems. European Journal of Operational Research 153(3), 564–580 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Feo, T., Resende, M.G.C.: Greedy Randomized Adaptive Search Procedures. Journal of Global Optimization 6(2), 109–133 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Fink, A.: Supply Chain Coordination by Means of Automated Negotiations Between Autonomous Agents. In: Chaib-draa, B., Müller, J. (eds.) Multiagent based Supply Chain Management. SCI, vol. 28, pp. 351–372. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Jennings, N.R., Faratin, P., Lomuscio, A.R., Parsons, S., Sierra, C., Wooldridge, M.: Automated Negotiation: Prospects, Methods and Challenges. Group Decision and Negotiation 10(2), 199–215 (2001)CrossRefGoogle Scholar
  7. 7.
    Klein, M., Faratin, P., Sayama, H., Bar-Yam, Y.: Negotiating Complex Contracts. Group Decision and Negotiation 12(2), 111–125 (2003)CrossRefzbMATHGoogle Scholar
  8. 8.
    Klein: M., Faratin, P., Sayama, H., Bar-Yam, Y.: Negotiating Complex Contracts, MIT Sloan Working Paper No. 4196-01 (2007),
  9. 9.
    Lehmann, D., Callaghan, L., Shoham, Y.: Truth Revelation in Approximately Efficient Combinatorial Auctions. Journal of the ACM 49(5), 577–602 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Milgrom, P.R.: Putting Auction Theory to Work. Cambridge University Press, Cambridge (2004)CrossRefGoogle Scholar
  11. 11.
    Nisan, N., Ronen, A.: Algorithmic Mechanism Design. Games and Economic Behavior 35, 166–196 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Nisan, N., Ronen, A.: Computationally Feasible VCG Mechanisms. Journal of Artificial Intelligence Research 29, 19–47 (2007)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Pekec, A., Rothkopf, M.H.: Combinatorial Auction Design. Management Science 49(11), 1485–1503 (2003)CrossRefzbMATHGoogle Scholar
  14. 14.
    Sandholm, T.: Distributed Rational Decision Making. In: Weiβ, G. (ed.) Multiagent Systems: A Modern Introduction to Distributed Artificial Intelligence, pp. 201–258. MIT Press, Cambridge (1999) Google Scholar
  15. 15.
    Sandholm, T., Suri, S., Gilpin, A., Levine, D.: Winner Determination in Combinatorial Auction Generalizations. In: Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS (2002)Google Scholar
  16. 16.
    Vickrey, W.: Counterspeculation, Auctions, and Competitive Sealed Tenders. The Journal of Finance 16(1), 8–37 (1961)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Zurel, E., Nisan, N.: An Efficient Approximate Allocation Algorithm for Combinatorial Auctions. In: Proceedings of the 3rd ACM Conference on Electronic Commerce (EC 2001), pp. 125–136 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Fabian Lang
    • 1
  • Andreas Fink
    • 1
  1. 1.Helmut Schmidt University HamburgGermany

Personalised recommendations