Skip to main content

Advertisement

Springer Nature Link
Log in

Fuzzy Constraint-Based Agent Negotiation

  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Conflicts between two or more parties arise for various reasons and perspectives. Thus, resolution of conflicts frequently relies on some form of negotiation. This paper presents a general problem-solving framework for modeling multi-issue multilateral negotiation using fuzzy constraints. Agent negotiation is formulated as a distributed fuzzy constraint satisfaction problem (DFCSP). Fuzzy constrains are thus used to naturally represent each agent’s desires involving imprecision and human conceptualization, particularly when lexical imprecision and subjective matters are concerned. On the other hand, based on fuzzy constraint-based problem-solving, our approach enables an agent not only to systematically relax fuzzy constraints to generate a proposal, but also to employ fuzzy similarity to select the alternative that is subject to its acceptability by the opponents. This task of problem-solving is to reach an agreement that benefits all agents with a high satisfaction degree of fuzzy constraints, and move towards the deal more quickly since their search focuses only on the feasible solution space. An application to multilateral negotiation of a travel planning is provided to demonstrate the usefulness and effectiveness of our framework.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Bradshaw Jeffrey M. Software Agents. AAAI/The MIT Press, 1997.

  2. Huhns Michnel N, Munindar P Singh (eds.). Readings in Agents. San Francisco, California: Morgan Kaufmann Publishers, Inc., 1998.

    Google Scholar 

  3. Pruitt D G. Negotiation Behavior. Academic Press, New York, 1981.

    Google Scholar 

  4. Kersten Gregory E, Gordon Lo. Negotiation support systems and software agents in e-business negotiations. In The First International Conference on Electronic Business, Hong Kong, December, 2001, pp.19–21.

  5. Rosenschein J S, Zlotkin G. Rules of Encounter: Designing Conventions for Automated Negotiation Among Computers. Cambridge, Massachusetts: MIT Press, 1994.

    Google Scholar 

  6. Ren Z, Anumba C J, Ugwu O O. Negotiation in a multi-agent system for construction claims negotiation. Applied Artificial Intelligence, 2002, 16: 359–394.

    Article  Google Scholar 

  7. Oliver Jim R. On artificial agents for negotiation in electronic commerce. In Proc. the 29th Annual Hawaii International Conference on System Sciences, 1996, pp.337–346.

  8. Eaton P S, Freuder E C, Wallace R J. Constraints and agents: Confronting Ignorance. AI Magazine, 1998, 19(2): 51–65.

    Google Scholar 

  9. Zeng D, Sycara K. Bayesian learning in negotiation. Internat. J. Human-Computer Stud., 1998, 48(1): 125–141.

    Article  Google Scholar 

  10. Choi Samuel P M, Liu Jiming, Chan Sheung-Ping. A genetic agent-based negotiation system. Computer Networks, 2001, 37: 195–204.

    Article  Google Scholar 

  11. Sycara K. Multi-agent compromise via negotiation. In {Distributed Artificial Intelligence}, Vol. 2. Gasser L, Huhns M (eds.), Morgan Kaufmann, San Mateo, CA, 1989, pp.119–139.

    Google Scholar 

  12. Sathi A, Fox M. Constraint-directed negotiation of resource reallocation. In Distributed Artificial Intelligence, Vol. 2. Gasser L, Huhns M (eds.), Morgan Kaufmann, San Mateo, CA, 1989, pp.163–195.

    Google Scholar 

  13. Matos Noyda, Carles Sierra. Evolutionary computing and negotiating agents. Agent Mediated Electronic Commerce, AMET-98, In Lecture Notes in Artificial Intelligent 1571, 1998, pp.126–150.

  14. Barbuceanu M, Lo W-K. A multi-attribute utility theoretic negotiation architecture for electronic commerce. In Proc. the Fourth International Conference on Autonomous Agents, 2000, pp.239–246.

  15. Luo Xudong, Nicholas R Jennings, Nigel Shadbolt et al. A fuzzy constraint based model for bilateral multi-issue negotiations in semi-competitive environments. Artificial Intelligence, 2003, 148: 53–102.

    Article  MathSciNet  Google Scholar 

  16. Kowalczyk R, Bui V. FeNAs: A fuzzy e-negotiation agents system. In Proc. the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence (CIFEr 2000), New York, 2000, pp.26–29.

  17. Faratin P, Sierra C, Jennings N R. Using similarity criteria to make trade-offs in automated negotiation. Artificial Intelligence, 2002, 142(2): 205–237.

    Article  MathSciNet  Google Scholar 

  18. Zadeh L A. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1978, 1: 3–28.

    Article  Google Scholar 

  19. Lai R. Fuzzy constraint processing [Dissertation]. NCSU, Raleigh, N. C., 1992.

  20. Dubois D, Fragier H, Prade H. Propagation and satisfaction of flexible constraints. In {Fuzzy Sets, Neural Networks and Soft Computing, Van Nostrand Reinhold}, Yager R, Zadeh L (eds.), New York, 1994, pp.166–187.

  21. Bowen J, Lai R, Bahler D. Lexical imprecision in fuzzy constraint networks. In Proc. AAAI-92, San Jose, CA, 1992, pp.616–621.

  22. Saaty T L. The Analytic Hierarchy Process. McGraw-Hill, New York, 1980.

    Google Scholar 

  23. Yager R R, Filev D P. Essential of Fuzzy Modeling and Control. John Wiley & Sons, New York, 1994.

    Google Scholar 

  24. Liu X. Entropy, distance measure and similarity measure of fuzzy sets and their relations. Fuzzy Sets and Systems, 1992, 52: 305–318.

    Article  Google Scholar 

  25. Finin T, McKay D, McEntire R. KQML—A language and protocol for knowledge and information exchange. Technical Report CS-94-02, Computer Science Department, University of Maryland, UMBC, Baltimore MD 21228, 1994.

  26. Searle J. Speech Acts. Cambridge University Press, 1969.

  27. Bellman R E, Zadeh L A. Decision making in a fuzzy environment. Management Science, 1970, 17: 141–164.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Management, Ching Yun University, Chung-Li

    Menq-Wen Lin

  2. Department of Computer Science and Engineering, Yuan Ze University, Chung-Li

    K. Robert Lai & Ting-Jung Yu

Authors
  1. Menq-Wen Lin
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. K. Robert Lai
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Ting-Jung Yu
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Menq-Wen Lin.

Additional information

Menq-Wen Lin was born in 1962. He received his M.S. degree in computer science from New Jersey Institute of Technology, and Ph.D. degree in computer science and engineering from Yuan Ze University, in 2004. In 1990, he joined Ching Yun University, where he is now an assistant professor. His current research interests include data mining and agent technologies.

K. Robert Lai was born in 1955. He received his M.S. degree in computer science from Ohio State University, in 1982, and Ph.D. degree in computer science from North Carolina State University, in 1992. In 1994, he joined Yuan Ze University, where he is now an associate professor. His current research interests are in agent technologies, and wireless networking.

Ting-Jung Yu was born on August 31, 1966. He received his M.S. degree in resource management from “National Defence Management College” in 1993. He is presently working toward the Ph.D. degree in computer science and engineering at Yuan Ze University. His current research interests include agent technologies and fuzzy constraints.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lin, MW., Lai, K.R. & Yu, TJ. Fuzzy Constraint-Based Agent Negotiation. J Comput Sci Technol 20, 319–330 (2005). https://doi.org/10.1007/s11390-005-0319-3

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-005-0319-3

Keywords