Advertisement

The Foundations of Information Push and Pull

  • George Cybenko
  • Brian Brewington
Chapter
Part of the The IMA Volumes in Mathematics and its Applications book series (IMA, volume 107)

Abstract

Information push and information pull have recently emerged as useful concepts to describe the operation of distributed information resources. Information push, in particular, is becoming closely associated with intelligent agent functionality. Loosely speaking, if a user requests and receives a very specific piece of information, this is information pull. If information is sent in anticipation of the user’s need, or the agent’s response includes information not directly solicited, then the situation is characterized as information push. Intuitively, junk mail (electronic or paper), television newscasts and wirefeeds are examples of information push. New web services such as Pointcast and Informant are examples of more selective push technologies. Web browsing, library searches, and telephone white pages are traditional examples of information pull. Clearly, these categorizations can be ambiguous and are easily lost in semantics. The main goal of this paper is to formalize these concepts and describe a mathematical framework around which further work can be more precise. Specifically, we develop a stochastic framework based on Markov models to describe an ambient environment and an agent system. Depending on the relationships between the environment, the agent and the user’s performance criterion, a continuum of possible information push and pull scenarios can be described. Some basic analytic results concerning the operation of a push/pull information system are derived.

Key words

information retrieval information dissemination push pull 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    A. Bensoussan. Stochastic Control of Partially Observable Systems. Cambridge University Press, Cambridge, UK, 1992.zbMATHCrossRefGoogle Scholar
  2. [2]
    D.P. Bertsekas. Dynamic Programming and Optimal Control. Athena Scientific, Belmont, MA, 1996.Google Scholar
  3. [3]
    D.H. Blackwell and D.A. Blackwell. Theory of Games and Statistical Decisions. Dover, New York, 1980.Google Scholar
  4. [4]
    J.M. Bradshaw. Software Agents. MIT Press, Cambridge, MA, 1997.Google Scholar
  5. [5]
    D.N. Chorafas. Agent Technology Handbook. McGraw Hill, New York, 1997.Google Scholar
  6. [6]
    G. Cybenko et al. Q-Learning: A tutorial and extensions. Mathematics in Artificial Neural Networks and Applications, Oxford, UK, 1995.Google Scholar
  7. [7]
  8. [8]
    D. Kotz et al. Agent Tcl: Targeting the needs of mobile computers. IEEE Internet Computing, 1:58–68, August 1997.CrossRefGoogle Scholar
  9. [9]
    H. Kushner. Introduction to Stochastic Control. Holt, Rinehart and Winston, New York, 1997.Google Scholar
  10. [10]
    H.R. Lewis and C.H. Papadimitriou. Elements of the Theory of Computation. Prentice-Hall, Englewood Cloffs, NJ, 1981.Google Scholar
  11. [11]
    P. Maes. Agents that reduce work and information overload. Communications of the ACM, 37(7):30–40, 1994.CrossRefGoogle Scholar
  12. [12]
    R. Moore and L.L. Lesnick. Creating Cool Intelligent Agents for the Net. IDG Books Worldwide, San Jose, CA, 1996.Google Scholar
  13. [13]
    A. Nerode. Linear automaton transformations. Proc. Amer. Math. Soc., 9:541–544, 1958.MathSciNetzbMATHCrossRefGoogle Scholar
  14. [14]
    D. Riecken. Special issue on intelligent agents. Communications of the ACM, 37(7):18–22, 1994.CrossRefGoogle Scholar
  15. [15]
    L. Spector. Automatic generation of intelligent agent programs. IEEE Intelligent Systems, 12(1):3–4, 1997.Google Scholar
  16. [16]
    I.H. Witten, A. Moffat, and T.C. Bell. Managing Gigabytes. Van Nostrand Reinhold, New York, 1994.zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • George Cybenko
    • 1
  • Brian Brewington
    • 2
  1. 1.Dartmouth CollegeThayer School of EngineeringHanoverUSA
  2. 2.Dartmouth CollegeThayer School of EngineeringHanoverUSA

Personalised recommendations