Skip to main content
Log in

Neural networks-based adaptive bidding with the contract net protocol in multi-robot systems

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

This paper investigates the effectiveness of using the Contract Net Protocol, an auction type system, for controlling task allocation among a group of robots, and presents and evaluates a strategy of using Artificial Neural Networks to formulate adaptive bids within the framework of the Contract Net Protocol. The robots were used in a foraging environment and showed that excellent communication among robots leads to a need for a social control mechanism for managing the robots, such as the Contract Net Protocol. The experiments also confirmed that a moderate benefit can be gained by using adaptive bidding within the framework of the Contract Net Protocol.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Assoun MH (1995) Fundamentals of artificial neural networks. MIT Press, Cambridge

    Google Scholar 

  2. Bond AH, Gasser L (1988) Distributed artificial intelligence. Morgan Kaufmann, San Francisco

    Google Scholar 

  3. Cao YU, Fukunaga AS, Kahng A (1997) Cooperative mobile robotics: antecendents and directions. Auton Robots 4:7–27

    Article  Google Scholar 

  4. Cheng B, Titterington DM (1994) Neural networks: a review from a statistical perspective. Stat Sci 9(1):2–30

    Article  MATH  MathSciNet  Google Scholar 

  5. comp.ai.neural-nets. How many hidden units should I use? http://www.faqs.org/faqs/ai-faq/neural-nets/part3/index.html. Accessed May 2007

  6. Davis R, Smith RG (1988) Negotiation as a metaphor for distributed problem solving. In: Bond AH, Gasser L (eds) Distributed artificial intelligence. Morgan Kaufmann, San Francisco, pp 333–356

    Google Scholar 

  7. Ebay. http://pages.ebay.com/help/buy/buyer-multiple.html. Accessed May 2007

  8. Geman S, Bienenstock E, Doursat R (1991) Neural networks and the bias/variance dilemma. Neural Comput 4:1–58

    Article  Google Scholar 

  9. Gerkey BP, Mataric MJ (2002) A market-based formulation of sensor-actuator network coordination. In: Proceedings of the AAAI spring symposium on intelligent embedded and distributed systems, Palo Alto, CA, March 2002, pp 21–26

  10. Gerkey BP, Mataric MJ (2002) Sold!: auction methods for multirobot coordination. IEEE Trans Robot Autom 18(5):758–768

    Article  Google Scholar 

  11. Glasius R, Komoda A, Gielen SC (1995) Neural network dynamics for path planning and obstacle avoidance. Neural Netw 8(1):125–133

    Article  Google Scholar 

  12. Golfarelli M, Maio D, Rizzi S (1997) Multi-agent path planning based on task-swap negotiation. In: Proceedings of the 16th UK planning and scheduling SIG workshop. Durham, pp 69–82

  13. Gun-Tactyx. http://gameprog.it/hosted/guntactyx/. Accessed May 2007

  14. IBM. http://www-128.ibm.com/developerworks/java/library/j-robocode/. Accessed May 2007

  15. Joone. http://www.joone.org/. Accessed May 2007

  16. Kensler JA (2007) On adaptive bidding with the contract net protocol for task allocation in multi-robot systems. MS thesis, Computer Science, University of Kansas, May 2007

  17. Lemaire T, Alami R, Lacroix S (2004) A distributed tasks allocation scheme in multi-UAV context. In: Proceedings of the IEEE international conference on robotics and automation, vol 4, New Orleans, LA, April 2004, pp 3622–3627

  18. McCullagh P, Nelder JA (1989) Generalized linear models, 2nd edn. Chapman & Hall, London

    MATH  Google Scholar 

  19. Moses Y, Tennenholtz M (1995) Artificial social systems. Comput Artif Intell 14(6):533–562

    MathSciNet  Google Scholar 

  20. Muller B, Reinhardt J (1990) Neural networks: an introduction. Springer, New York

    Google Scholar 

  21. Parker LE (1998) ALLIANCE, an architecture for fault-tolerant multi-robot cooperation. IEEE Trans Robot Autom 14(2):220–240

    Article  Google Scholar 

  22. RoboCode. http://robocode.sourceforge.net/docs/robocode/. Accessed May 2007

  23. Russell S, Norvig P (2003) Artificial intelligence: a modern approach, 2nd edn. Prentice Hall, Pearson Education, Upper Saddle River

    Google Scholar 

  24. Sandholm T, Lesser V (1996) Advantages of a Leveled commitment contracting protocol. In: Proceedings of the thirteenth national conference on artificial intelligence. AAAI Press, Menlo Park, pp 126–133

    Google Scholar 

  25. Sandholm, Lesser (1995) Issues in automated negotiation and electronic commerce: extending the contract net framework. In: Proceedings of the 1st international conference on multi-agent systems, San Francisco, CA, pp 328–335

  26. Scerri P, Vincent R, Mailler RT (2005) Coordination of large-scale multiagent systems. Springer, New York

    Google Scholar 

  27. Shehory O, Kraus S (1998) Methods for task allocation via agent coalition formation. Artif Intell 101(1–2):165–200

    Article  MATH  MathSciNet  Google Scholar 

  28. Shoham Y, Tennenholtz M (1994) Co-learning and the evolution of social activity. Technical Report STAN-CS-TR-94-1511, Stanford University

  29. Shoham Y, Tennenholtz M (1992) On the synthesis of useful social laws for artificial agent societies. In: Proceedings of the tenth national conference on artificial intelligence, San Diego, pp 704–709

  30. Smith RG (1980) The contract net protocol: high-level communication and control in a distributed problem solver. IEEE Trans Comput C-29(12):1104–1113

    Article  Google Scholar 

  31. TeamBots. http://www.teambots.org/. Accessed May 2007

  32. Weiss G (1999) Multiagent systems: a modern approach to distributed artificial intelligence. MIT Press, Cambridge

    Google Scholar 

  33. Zlot RM, Stentz A, Dias MB, Thayer S (2002) Multi-robot exploration controlled by a market economy. In: Proceedings of the IEEE international conference on robotics and automation, vol 3, pp 3016–3023

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Arvin Agah.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kensler, J.A., Agah, A. Neural networks-based adaptive bidding with the contract net protocol in multi-robot systems. Appl Intell 31, 347–362 (2009). https://doi.org/10.1007/s10489-008-0131-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-008-0131-1

Keywords

Navigation