Advertisement

Constraint Satisfaction for Modelling Scalable Electronic Catalogs

  • Marc Torrens
  • Boi Faltings
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1991)

Abstract

Many information systems are used in a problem solving context. Examples are travel planning systems, catalogs in electronic commerce, or agenda planning systems. They can be made more useful by integrating problem-solving capabilities into the information systems. This poses the challenge of scalability: when hundreds of users access a server at the same time, it is important to avoid excessive computational load.

We present the concept of smart clients: lightweight problem-solving agents based on constraint satisfaction which can carry out the computation - and communication-intensive tasks on the user’s computer. We present an example of an air travel planning system based on this technology.

Keywords

Constraint Satisfaction Constraint Satisfaction Problem Planning Agent Travel Planning Airline Company 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [Cho94]
    Berthe Y. Choueiry. Abstraction Methods for Resource Allocation. PhD thesis, Swiss Federal Institute of Technology in Lausanne, 1994.Google Scholar
  2. [FIP98]
    FIPA. Foundation of Intelligent Physical Agents. http://www.fipa.org, 1998.
  3. [Fox87]
    Mark Fox. Constraint-Directed Search: A Case Study of Job-Shop Scheduling. Morgan Kaufmann Publishers, Inc., Pitman, London, 1987.zbMATHGoogle Scholar
  4. [FW92]
    Eugene C. Freuder and Richard J. Wallace. Partial Constraint Satisfaction. Artificial Intelligence, 58(1):21–70, 1992.CrossRefMathSciNetGoogle Scholar
  5. [Jan98]
    Jango. Excite Product Finder. http://www.jango.com, 1998.
  6. [KvB97]
    Grzegorz Kondrak and Peter van Beek. A Theoretical Evaluation of Selected Backtracking Algorithms. Artificial Intelligence, 89:365–387, 1997.zbMATHCrossRefMathSciNetGoogle Scholar
  7. [LHL97]
    G. Linden, S. Hanks, and N. Lesh. Interactive Assessment of User Preference Models: The Automated Travel Assistant. In Proceedings of Sixth International Conference on User Modeling, 1997.Google Scholar
  8. [Mac77]
    Alan K. Mackworth. Consistency in Networks of Relations. Artificial Intelligence, 8:99–118, 1977.zbMATHCrossRefGoogle Scholar
  9. [SF89]
    A. Sathi and M. S. Fox. Constraint-Directed Negotiation of Resource Allocations. In L. Gasser and M. Huhns, editors, Distributed Artificial Intelligence Volume II, pages 163–194. Pitman Publishing: London and Morgan Kaufmann: San Mateo, CA, 1989.Google Scholar
  10. [SF96]
    Daniel Sabin and Eugene C. Freuder. Configuration as Composite Constraint Satisfaction. In Proceedings of the Artificial Intelligence and Manufacturing Research Planning Workshop, pages 153–161, 1996.Google Scholar
  11. [SM89]
    Felix Freyman Sanjay Mittal. Towards a generic model of configuration tasks. pages 1395–1401, 1989.Google Scholar
  12. [Ste81]
    Mark Stefik. Planning with constraints (MOLGEN: Part 1). Artificial Intelligence, 16(2):111–140, 1981.CrossRefGoogle Scholar
  13. [Tsa93]
    Edward Tsang. Foundations of Constraint Satisfaction. Academic Press, London, UK, 1993.Google Scholar
  14. [vB95]
    Peter van Beek. CSPLib: a CSP library written in C language. ftp://ftp.cs.ualberta.ca/pub/vanbeek/software, 1995.

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Marc Torrens
    • 1
  • Boi Faltings
    • 1
  1. 1.Artificial Intelligence Laboratory (LIA)Swiss Federal Institute of Technology (EPFL)LausanneSwitzerland

Personalised recommendations