Mobile Agent Based Auctionlike Negotiation in Internet Retail Commerce

  • XiaoFeng Wang
  • Xun Yi
  • Ramayya Krishnan
  • Chee Kheong Siew
  • Pradeep K. Khosla
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 105)


As an intelligent and agile software, mobile agent is promising to boost the flexibility and performance of current Internet retail commerce. However, due to the constrained computational resources and security concerns, current first-generation shopping agents have only limited capacities to conduct negotiation. On the other hand, though online auction exhibits attractive features for retail negotiation such as fairness and openness, it suffers from the problems such as reversed consumer-buyer relation and low performance. In this chapter, we present a novel trading scheme which combines together the favorable features of mobile agent and traditional English auction. The scheme implements an auctionlike negotiation protocol to rationalize the buyer-seller relation, keep the dominant strategies in the English auction and simplify the security measures to protect mobile trade agents during the trading process. Based on the protocol, an online learning algorithm supported by clustering techniques and fuzzy sets further helps to improve the system performance and flexibility. In this chapter, we report our in-depth research on the proposed scheme. Both theoretic and empirical results are presented.


Mobile Agent Online Auction Negotiation Protocol English Auction Automatic Negotiation 
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.
    Ada D, Kibler D, Albert M (1991) Instance-based learning algorithm. In: Proceedings of International conference of Machine learningGoogle Scholar
  2. 2.
    Beam C, Segev A, Shanthikumar G (1996) Electronic Negotiation through Internet-based Auctions. CITM Working Paper 96-WP-1019, available at
  3. 3.
    Borenstein N (1994) Email with a Mind of its Own: The Safe-Tcl Language for Enabled Mail. In: Proceedings of IFIP WG 65 ConferenceGoogle Scholar
  4. 4.
    Forrester Research Report (1997) On-Line Internet SpendingGoogle Scholar
  5. 5.
    Guttman R, Maes P (1998) Cooperative vs. Competitive Multi-Agent Negotiations in Retail Electronic Commerce. In: Proceedings of the Second International Workshop on Cooperative Information Agents. Paris, FranceGoogle Scholar
  6. 6.
    Guttman R, Maes P (1998) Agent-mediated Integrative Negotiation for Retail Electronic Commerce. In: Proceedings of Workshop on Agent Mediated Electronic Trading. Minneapolis, Minnesota, USAGoogle Scholar
  7. 7.
    Klir G, Clair U, Yuan B (1997) Fuzzy set theory: foundations and applications. Prentice HallGoogle Scholar
  8. 8.
    McAfee R P, McMillan J (1987) Auction and Bidding. Journal of Economic Literature: 699–738Google Scholar
  9. 9.
    Sandholm T (1993) An Implementation of the Contract Net Protocol Based on Marginal Cost Calculations. In: Proceedings of the Eleventh National Conference on Artificial Intelligence. pp 256–262Google Scholar
  10. 10.
    Sanjiv R D, Rangarajan K. S (1997) Auction Theory: A Summary with Application to Treasury Markets. Working Paper 5873. National Bureau of Economic ResearchGoogle Scholar
  11. 11.
    Schewartz E, Webonomics (1997) Nine Essential Principle for Growing Your Bussiness on the World Wide Web. Broadway BooksGoogle Scholar
  12. 12.
    Wang X F, Yi X, Lam K Y, Zhang C Q, Okamoto E (1999) Secure Agent-Mediated Auctionlike Negotiation protocol for Internet Retail Commerce. In: Proceedings of Workshop on Cooperative Information Agents. Springer-Verlag LNAI 1652 (An extended version of the paper is available at: xiaofeng/saman.pdf)Google Scholar
  13. 13.
    Wang X F, Zhang S C, Khosla P K, Kiliccote H, Zhang C Q, Lain K Y (2000) Anytime algorithm for agent-mediated merchant information gathering. In: Proceedings of ACM International Conference on Autonomous Agents. ACM pressGoogle Scholar
  14. 14.
    Zeng D, Sycara K (1996) Bayesian Learning in Negotiation. In: Working Notes of the AAAI 1996 Stanford Spring Symposium Series on Adaptation, Coevoluation, and Learning in Multiagent SystemsGoogle Scholar
  15. 15.
    Zlotkin G, Rosenschein J S (1989) Negotiation and Task Sharing among Autonomous Agents in Cooperative Domains. In: Proceedings of the 11th International Joint Conference on Artificial Intelligence. Detroit, Michigan, pp 912–917Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • XiaoFeng Wang
    • 1
  • Xun Yi
    • 2
  • Ramayya Krishnan
    • 3
  • Chee Kheong Siew
    • 2
  • Pradeep K. Khosla
    • 1
  1. 1.Department of Electrical and Computer EngineeringCarnegie Mellon UniversityPittsburghUSA
  2. 2.Nanyang Technological UniversitySingapore
  3. 3.Heinz SchoolCarnegie Mellon UniversityUSA

Personalised recommendations