Advertisement

Scilog: A Language for Scientific Processes and Scales

  • Joseph Phillips
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2843)

Abstract

We present Scilog, an experimental knowledge base to facilitate scientific discovery and reasoning. Scilog extends Prolog by supporting (1) dedicated predicates for specifying and querying knowledge about scientific processes, (2) the different scales at which processes may be manifested, and (3) the domains to which values belong. Scilog is meant to invoke more specialized algorithms and to be called by high-level discovery routines. We test Scilog’s ability to support such routines with a simple search through the space of geophysical models.

Keywords

Reasoning System Process Instance Composite Process Geophysical Model Process Class 
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. 1.
    Collier, M.: A Land in Motion: California’s San Andreas Fault. University of California Press, Berkeley (1999)Google Scholar
  2. 2.
    DeMets, C., Gordon, R.G., Argus, D.F., Stein, S.: Current plate motions. Geophys. J. Int. 101, 425–478 (1990)CrossRefGoogle Scholar
  3. 3.
    Jordan, B.: Global Plate Motion Models (2002), http://people.whitman.edu/~jordanbt/platemo.html
  4. 4.
    Jordan, T.H., Minster, J.B.: Measuring Crustal Deformation in the AmericanWest. Scientific American (August 1988)Google Scholar
  5. 5.
    Kuipers, B.: Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge. MIT Press, Cambridge (1994)Google Scholar
  6. 6.
    Langley, P., Sanchez, J., Todorovski, L., Dzeroski, S.: Inducing Process Models from Continuous Data. In: ICML (2002)Google Scholar
  7. 7.
    Phillips, J.: Representation Reducing Heuristics for Semi-Automated Scientific Discovery. Ph.D. Thesis, University of Michigan (2000)Google Scholar
  8. 8.
    Phillips, J.: Towards a Method of Searching a Diverse Theory Space for Scientific Discovery. In: Jantke, K.P., Shinohara, A. (eds.) DS 2001. LNCS (LNAI), vol. 2226, p. 304. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  9. 9.
    Valdes-Perez, R.: Machine discovery in chemistry: new results. Artificial Intelligence 74(1), 191–201 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Joseph Phillips
    • 1
  1. 1.Telecommunications and Information SystemsDePaul University, School of Computer ScienceChicagoUSA

Personalised recommendations