Skip to main content

Sequencing of Contract Types for Anytime Task Reallocation

  • Conference paper
  • First Online:
Agent Mediated Electronic Commerce (AMET 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1571))

Included in the following conference series:

Abstract

Task (re)allocation is a key problem in multiagent systems. Several different contract types have been introduced to be used for task reallocation: original, cluster, swap, and multiagent contracts. Instead of only using one of these contract types, they can be interleaved in a sequence of contract types. This is a powerful way of constructing algorithms that find the best solution reachable in a bounded amount of time. The experiments in this paper study how to best sequence the different contract types.

We show that the number of contracts performed using any one contract type does not necessarily decrease over time as one might expect. The reason is that contracts often play the role of enabling further contracts. The results also show that it is clearly profitable for the agents to mix contract types in the sequence. Sequences of different contract types reach a solution significantly closer to the global optimum and in a shorter amount of time than sequences with only one contract type. However, the best sequences consist only of two interleaved contract types: original and cluster contracts. This allows us to provide a clear prescription about protocols for anytime task reallocation.

Supported by NSF CAREER award IRI-9703122 and NSF grant IRI-9610122.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Reference

  1. M. R. Andersson. Performance of leveled commitment protocols for automated negotiation: An empirical study. Master’s thesis, Royal Institute of Technology, Stockholm, Sweden, 1998.

    Google Scholar 

  2. M. R. Andersson and T. W. Sandholm. Contract types for optimal task allocation: II experimental results. Technical Report WUCS-97-36, Washington University, Department of Computer Science, 1997.

    Google Scholar 

  3. M. R. Andersson and T. W. Sandholm. Leveled commitment contracting among myopic individually rational agents. Technical Report WUCS-97-47, Washington University, Department of Computer Science, 1997.

    Google Scholar 

  4. M. R. Andersson and T. W. Sandholm. Contract types for satisficing task allocation: II experimental results. In AAAI Spring Symposium Series: Satisficing Models, pages 1–7, Stanford University, CA, Mar. 1998.

    Google Scholar 

  5. M. R. Andersson and T. W. Sandholm. Leveled commitment contracting among myopic individually rational agents. In Proceedings of the Third International Conference on Multi-Agent Systems (ICMAS), pages 26–33, Paris, France, July 1998.

    Google Scholar 

  6. P. R. Cohen. Empirical Methods for Artificial Intelligence. MIT Press, 1995.

    Google Scholar 

  7. T. H. Cormen, C. E. Leiserson, and R. L. Rivest. Introduction to Algorithms. MIT Press, 1990.

    Google Scholar 

  8. R. Kalakota and A. B. Whinston. Frontiers of Electronic Commerce. Addison-Wesley Publishing Company, Inc, 1996.

    Google Scholar 

  9. R. E. Korf. Depth-first iterative-deepening: An optimal admissible tree search. Artificial Intelligence, 27(1):97–109, 1985.

    Article  MATH  MathSciNet  Google Scholar 

  10. R. P. McAfee and J. McMillan. Analyzing the airwaves auction. Journal of Economic Perspectives, 10(1):159–175, 1996.

    Google Scholar 

  11. H. Raiffa. The Art and Science of Negotiation. Harvard Univ. Press, Cambridge, Mass., 1982.

    Google Scholar 

  12. J. S. Rosenschein and G. Zlotkin. Rules of Encounter: Designing Conventions for Automated Negotiation among Computers. MIT Press, 1994.

    Google Scholar 

  13. 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), pages 256–262, Washington, D.C., July 1993.

    Google Scholar 

  14. T. W. Sandholm. Limitations of the Vickrey auction in computational multiagent systems. In Proceedings of the Second International Conference on Multi-Agent Systems (ICMAS), pages 299–306, Keihanna Plaza, Kyoto, Japan, Dec. 1996.

    Google Scholar 

  15. T. W. Sandholm. Negotiation among Self-Interested Computationally Limited Agents. PhD thesis, University of Massachusetts, Amherst, 1996. Available at http://www.cs.wustl.edu/~sandholm/dissertation.ps.

  16. T. W. Sandholm. Contract types for satis_cing task allocation: I theoretical results. In AAAI Spring Symposium Series: Satisficing Models, pages 68–75, Stanford University, CA, Mar. 1998.

    Google Scholar 

  17. T. W. Sandholm and V. R. Lesser. Advantages of a leveled commitment contracting protocol. In Proceedings of the National Conference on Artificial Intelligence (AAAI), pages 126–133, Portland, OR, Aug. 1996. Extended version: University of Massachusetts at Amherst, Computer Science Department technical report 95-72.

    Google Scholar 

  18. 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), pages 632–638, Nagoya, Japan, Aug. 1997.

    Google Scholar 

  19. A. Sathi and M. Fox. Constraint-directed negotiation of resource reallocations. In M. N. Huhns and L. Gasser, editors, Distributed Artificial Intelligence, volume 2 of Research Notes in Artificial Intelligence, chapter 8, pages 163–193. Pitman, 1989.

    Google Scholar 

  20. S. Sen. Tradeoffs in Contract-Based Distributed Scheduling. PhD thesis, Univ. of Michigan, 1993.

    Google Scholar 

  21. R. G. Smith. The contract net protocol: High-level communication and control in a distributed problem solver. IEEE Transactions on Computers, C-29(12):1104–1113, Dec. 1980.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Andersson, M.R., Sandholm, T.W. (1999). Sequencing of Contract Types for Anytime Task Reallocation. In: Noriega, P., Sierra, C. (eds) Agent Mediated Electronic Commerce. AMET 1998. Lecture Notes in Computer Science(), vol 1571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48835-9_4

Download citation

  • DOI: https://doi.org/10.1007/3-540-48835-9_4

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65955-6

  • Online ISBN: 978-3-540-48835-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics