Agencies of Agents for Logistic Applications

  • Sonali Banerjee
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 105)


This paper presents an overview of current trends in Electronic Business (E-business), and discusses how an enterprise can use the Electronic Marketspace to its strategic advantage.

In this paper, we define an agency to be a multi-agent system created by integrating agents selected from a library of reusable agents that have formed a federation. A federation of agents comprises of a set of registered agents, which are themselves complete knowledge-based systems. The set of agents may be heterogeneous with respect to long-term knowledge, solution-evaluation criteria, or goals, as well as languages, algorithms, hardware requirements, etc.

An agency-based framework is presented for E-business in the domain of logistics and supply systems. To support the integration of heterogeneous and reusable agents into functional agent sets, a framework for the coalition of cooperating agents is needed. The role of cooperative information agents is discussed within the context of E-business.

Conflict is an integral part of problem solving in multi-agent systems and is often the focal point of interaction among agents. Rules about agent interaction can be used to mediate, mitigate or avoid conflict and as a control structure for agent activity to ensure global coherence of the agent set. This can be achieved by an executive agent that has an overall perspective of the other agents in the federation.

A facilitator, providing query and result refinement, together with, decision making tools that can be customized to take advantage of specific agents and agent-set characteristics, while maintaining a global view of the problem that needs to be solved can be integrated with the adoption of the agent architecture discussed here. The effectiveness of the facilitator as a coordination mechanism is in the specification of the rules that guide the task or service agents in problem solving and the meta-rules which instantiates appropriate knowledge sources. Several scenarios, with the prototype facilitator has been considered as proof of this concept.

Such logistic systems enjoy wide acceptance in commercial and military application domains, as witnessed by the phenomenal growth in Internet stocks for companies such as We show how agent-based systems provide scalable, configurable and evolutionary solutions for such applications.


Multiagent System Software Agent Electronic Commerce Logistic Application Internet Engineer Task Force 
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.


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  1. 1.
    Genesereth, M. and S.P. Ketchpel (1994),Software Agents.Communications of the ACM, 37(7): p. 48–53.Google Scholar
  2. 2.
    Metzger, P.a.S., W (1995),IP Authentication using Keyed MD5. Internet RFC 1828, Proposed Standards.Google Scholar
  3. 3.
    Atkinson, R. (1995),IP Encapsulating Security Payload. Internet RFC 1827, Proposed Standards.Google Scholar
  4. 4.
    Metzger, P., Kam, P and Simpson, W (1995),The ESP DES-CBC Transform. Internet RFC 1829, Proposed Standards.Google Scholar
  5. 5.
    Bhimani (1996),Securing the commercial Internet.Communications of the ACM,: p. 29–35.Google Scholar
  6. 6.
    Berners-Lee, T., et al.(1994),The World-Wide Web.Communications of the ACM, 37(8): p. 76–82.Google Scholar
  7. 7.
    Borenstein, N.S. (1996),Perils and pitfalls of practical cybercommerce. Communications of the ACM, 39(June): p. 36–44.CrossRefGoogle Scholar
  8. 8.
  9. 9.
    Klusch, M. and O. Shehory (1996).Coalition Formation Among Rational Information Agents. inSeventh European Workshop on Modelling Autonomous Agents in a Multi-Agent World (MAAMAW-96). Eindhoven, Netherlands: Springer-Verlag.Google Scholar
  10. 10.
    Maes, P., ed. (1990)Designing Autonomous Agents: Theory and Practice from Biology to Engineering and Back. Special Issues of Robotics and Autonomous Systems, The MIT Press: Cambridge, MA, London, England. 194.Google Scholar
  11. 11.
    G. Wiederhold, S. Jajodia, and W. Litwin (1991), “Dealing with Granularity of Time in Temporal Databases,” in Lecture Notes in Computer Science, vol. 498, R. Anderson and others, Eds.: Springer- Verlag, pp. 124–140.Google Scholar
  12. 12.
    G. Wiederhold, S. Jajodia, and W. Litwin (1993), “Integrating Temporal Data in a Heterogeneous Environment,” in Temporal Databases: Theory, Design, and Implementation, A. U. Tansel, S. Jajodia, and others, Eds.: Benjamin/Cummings, pp. 563–579.Google Scholar
  13. 13.
    G. Wiederhold (1996), “Foreword to Special Issue on the Intelligent Integration of Information,” Journal of Intelligent Information Systems, vol. 6, 2 /3, pp. 93–97.Google Scholar
  14. 14.
    Decker, Keith; Sycara, Katia (1997), Intelligent adaptive Information Agents. Jour. of Intelligent Info. Systems, 9, 239–260.Google Scholar
  15. 15.
    R. Davis and R. Smith (1983), “Negotiation as a Metaphor for Distributed Problem SolvingArtificial Intelligence, vol. 20, pp. 63–109.CrossRefGoogle Scholar
  16. 16.
    P. Maes, T. Darrell, B. Blumberg, and A. Pentland (1996), “The ALIVE System: Wireless, Full-Body Interaction with Autonomous Agents,” ACM Multimedia Systems.Google Scholar
  17. 17.
    K.-Y. Lai and T. W. Malone (1991), “Object Lens: Letting End-Users Create Cooperative Work Applications,” presented at Proceedings of CHI’91.Google Scholar
  18. 18.
    L. Kerschberg (1997), “Knowledge Rovers: Cooperative Intelligent Agent Support for Enterprise Information Architectures,” in Cooperative Information Agents, vol. 1202, Lecture Notes in Artificial Intelligence, P. Kandzia and M. Klusch, Eds. Berlin: Springer-Verlag, pp. 79–100.CrossRefGoogle Scholar
  19. 19.
    L. Kerschberg (1997), “The Role of Intelligent Agents in Advanced Information Systems,” in Advanced in Databases, vol. 1271, Lecture Notes in Computer Science, C. Small, P. Douglas, R. Johnson, P. King, and N. Martin, Eds. London: Springer-Verlag, pp. 1–22.CrossRefGoogle Scholar
  20. 20.
    S. Jajodia and L. Kerschberg (1997), “Advanced Transaction Models and Architectures,”. Norwall, MA: Kluwer Academic Publishers.CrossRefGoogle Scholar
  21. 21.
    S. Dao and B. Perry (1996), “Information Mediation in Cyberspace: Scalable Methods for Declarative Information Networks Journal of Intelligent Information Systems, vol. 6, 2/3, pp. 131–150.CrossRefGoogle Scholar
  22. 22.
    G. Wiederhold (1992), “The Roles of Artificial Intelligence in Information SystemsJournal of Intelligent Information Systems, vol. 1, pp. 35–56.CrossRefGoogle Scholar
  23. 23.
    G. Wiederhold (1996), “Foreword to Special Issue on the Intelligent Integration of Information Journal of Intelligent Information Systems, vol. 6, 2/3, pp. 93–97.CrossRefGoogle Scholar
  24. 24.
    Kerschberg, L. (1996),et al., Knowledge Rovers: A Family of Configurable Software Agents,inProposal Funded by DARPA’s Advanced Logistics Program.,Center for Information Systems Integration and Evolution, George Mason University, URL: Fairfax, VA.
  25. 25.
    Kerschberg, L. (1997),Knowledge Rovers: Cooperative Intelligent Agent Support for Enterprise Information Architectures, inLecture Notes in Computer Science, M. Klusch, Editor., Springer-Verlag.Google Scholar
  26. 26.
    A. Motro (1990), “FLEX: A Tolerant and Cooperative User Interface to DatabasesIEEE Transactions on Knowledge and Data Engineering, vol. 2, pp. 231–246.CrossRefGoogle Scholar
  27. 27.
    A. Motro (1993), “Accommodating Imprecision in Database Systems: Issues and Solutions,” InMultidatabase Systems: An Advanced Solution to Global Information Sharing, A. R. Hurson, M. W. Bright, and S. Pakzad, Eds.: IEEE Computer Society Press, pp. 381–386.Google Scholar
  28. 28.
    A. Motro (1993), “A Formal Framework for Integrating Inconsistent Answers from Multiple Information Sources,” Department of Information and Software Systems Engineering, George Mason University, Fairfax, VA, Technical Report ISSE-TR-93–106.Google Scholar
  29. 29.
    A. Motro (1994), “Intensional Answers to Database QueriesIEEE Transactions on Knowledge and Data Engineering, vol. 6, pp. 444–454.CrossRefGoogle Scholar
  30. 30.
    A. Motro (1994), “Management of Uncertainty in Database Systems,” InModern Database Systems: The Object Model, Interoperability and Beyond, W. Kim, Ed.: Addison-Wesley Publishing Company/ACM Press.Google Scholar
  31. 31.
    A. Motro (1995), “Multiplex: A Formal Model for Multidatabases and Its Implementation,” ISSE Department, George Mason University, Fairfax, VA, Technical Report ISSE-TR-95–10.Google Scholar
  32. 32.
    A. Motro (1995), “Responding with Knowledge,” InAdvances in Databases and Artificial Intelligence, Vol. 1: The Landscape of Intelligence in Database and Information Systems, vol. 1, L. Delcambre and F. Petry, Eds.: JAI Press.Google Scholar
  33. 33.
    A. Motro, D. Marks, and S. Jajodia (1994), “Aggregation in Relational Databases: Controlled Disclosure of Sensitive Information,” European Symposium on Research in Computer Security.Google Scholar
  34. 34.
    A. Motro and P. Smets (1996), “Uncertainty Management in Information Systems: from Needs to Solutions,”. Norwall, MA: Kluwer Academic Publishers, pp. 48Google Scholar
  35. 35.
    K. S. Decker (1995), “Environment Centered Analysis and Design of Coordination Mechanisms,” in Ph.D. Thesis, Department of Computer Science. Amherst, MA,: University of Massachusetts.Google Scholar
  36. 36.
  37. 37.
    Digicash, m
  38. 38.
    Bank, F.V.,

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© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Sonali Banerjee
    • 1
  1. 1.Union CityUSA

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