The Foundations of Information Push and Pull
- 173 Downloads
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 wordsinformation retrieval information dissemination push pull
Unable to display preview. Download preview PDF.
- D.P. Bertsekas. Dynamic Programming and Optimal Control. Athena Scientific, Belmont, MA, 1996.Google Scholar
- D.H. Blackwell and D.A. Blackwell. Theory of Games and Statistical Decisions. Dover, New York, 1980.Google Scholar
- J.M. Bradshaw. Software Agents. MIT Press, Cambridge, MA, 1997.Google Scholar
- D.N. Chorafas. Agent Technology Handbook. McGraw Hill, New York, 1997.Google Scholar
- G. Cybenko et al. Q-Learning: A tutorial and extensions. Mathematics in Artificial Neural Networks and Applications, Oxford, UK, 1995.Google Scholar
- Informant. http://informant.dartmouth.edu
- H. Kushner. Introduction to Stochastic Control. Holt, Rinehart and Winston, New York, 1997.Google Scholar
- H.R. Lewis and C.H. Papadimitriou. Elements of the Theory of Computation. Prentice-Hall, Englewood Cloffs, NJ, 1981.Google Scholar
- R. Moore and L.L. Lesnick. Creating Cool Intelligent Agents for the Net. IDG Books Worldwide, San Jose, CA, 1996.Google Scholar
- L. Spector. Automatic generation of intelligent agent programs. IEEE Intelligent Systems, 12(1):3–4, 1997.Google Scholar