Simulation of Negotiation Policies in Distributed Multiagent Resource Allocation

  • Hylke Buisman
  • Gijs Kruitbosch
  • Nadya Peek
  • Ulle Endriss
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4995)


In distributed approaches to multiagent resource allocation, the agents belonging to a society negotiate deals in small groups at a local level, driven only by their own rational interests. We can then observe and study the effects such negotiation has at the societal level, for instance in terms of the economic efficiency of the emerging allocations. Such effects may be studied either using theoretical tools or by means of simulation. In this paper, we present a new simulation platform that can be used to compare the effects of different negotiation policies and we report on initial experiments aimed at gaining a deeper understanding of the dynamics of distributed multiagent resource allocation.


Social Welfare Multiagent System Atomic Proposition Valuation Function Simulation Platform 
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.
    Chevaleyre, Y., Dunne, P.E., Endriss, U., Lang, J., Lemaître, M., Maudet, N., Padget, J., Phelps, S., Rodríguez-Aguilar, J.A., Sousa, P.: Issues in multiagent resource allocation. Informatica 30, 3–31 (2006)zbMATHGoogle Scholar
  2. 2.
    Cramton, P., Shoham, Y., Steinberg, R. (eds.): Combinatorial Auctions. MIT Press, Cambridge (2006)Google Scholar
  3. 3.
    Sandholm, T.W.: Contract types for satisficing task allocation: I Theoretical results. In: Proc. AAAI Spring Symposium: Satisficing Models (1998)Google Scholar
  4. 4.
    Endriss, U., Maudet, N., Sadri, F., Toni, F.: Negotiating socially optimal allocations of resources. Journal of Artificial Intelligence Research 25, 315–348 (2006)MathSciNetGoogle Scholar
  5. 5.
    Dunne, P.E., Wooldridge, M., Laurence, M.: The complexity of contract negotiation. Artificial Intelligence 164, 23–46 (2005)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Arrow, K.J., Sen, A.K., Suzumura, K. (eds.): Handbook of Social Choice and Welfare. North-Holland, Amsterdam (2002)Google Scholar
  7. 7.
    Endriss, U., Maudet, N.: Welfare engineering in multiagent systems. In: Engineering Societies in the Agents World IV. LNCS (LNAI), vol. 3071. Springer, Heidelberg (2004)Google Scholar
  8. 8.
    Andersson, M., Sandholm, T.W.: Contract type sequencing for reallocative negotiation. In: Proc. 20th International Conference on Distributed Computing Systems (ICDCS 2000). IEEE Press, Los Alamitos (2000)Google Scholar
  9. 9.
    Estivie, S.: Allocation de Ressources Multi-Agent: Théorie et Pratique. PhD thesis, Université Paris-Dauphine (2006)Google Scholar
  10. 10.
    Estivie, S., Chevaleyre, Y., Endriss, U., Maudet, N.: How equitable is rational negotiation? In: Proc. 5th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2006). ACM Press, New York (2006)Google Scholar
  11. 11.
    Moulin, H.: Axioms of Cooperative Decision Making. Cambridge University Press, Cambridge (1988)zbMATHGoogle Scholar
  12. 12.
    Lang, J.: Logical preference representation and combinatorial vote. Annals of Mathematics and Artificial Intelligence 42(1–3), 37–71 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Grabisch, M.: k-order additive discrete fuzzy measures and their representation. Fuzzy Sets and Systems 92, 167–189 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Leyton-Brown, K., Pearson, M., Shoham, Y.: Towards a universal test suite for combinatorial auction algorithms. In: Proc. 2nd ACM Conference on Electronic Commerce. ACM Press, New York (2000)Google Scholar
  15. 15.
    Hart, P., Nilsson, N., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics 4(2), 100–107 (1968)CrossRefGoogle Scholar
  16. 16.
    Sandholm, T.W.: Optimal winner determination algorithms. In: Cramton, P., Shoham, Y., Steinberg, R. (eds.) Combinatorial Auctions. MIT Press, Cambridge (2006)Google Scholar
  17. 17.
    Brams, S.J., Taylor, A.D.: Fair Division: From Cake-cutting to Dispute Resolution. Cambridge University Press, Cambridge (1996)zbMATHGoogle Scholar
  18. 18.
    Epstein, J.M., Axtell, R.L.: Growing Artificial Societies: Social Science from the Bottom Up. MIT Press, Cambridge (1996)Google Scholar
  19. 19.
    Tesfatsion, L., Judd, K. (eds.): Handbook of Computational Economics: Agent-Based Computational Economics. Elsevier, Amsterdam (2006)Google Scholar
  20. 20.
    Dunne, P.E.: Extremal behaviour in multiagent contract negotiation. Journal of Artificial Intelligence Research 23, 41–78 (2005)zbMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Hylke Buisman
    • 1
  • Gijs Kruitbosch
    • 1
  • Nadya Peek
    • 1
  • Ulle Endriss
    • 1
  1. 1.Artificial Intelligence ProgrammeUniversity of Amsterdam 

Personalised recommendations