Learning Qualitative Models of Physical and Biological Systems

Simon M. Garrett, George MacLeod Coghill, Ashwin Srinivasan, Ross D. King

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Citations (Scopus)

Abstract

We present a qualitative model-learning system, Qoph, developed for application to scientific discovery problems. Qoph learns the structural relations between a set of observed variables. It has been shown capable of learning models with intermediate (unmeasured) variables, and intermediate relations, under different levels of noise, and from qualitative or quantitative data. A biological application of Qoph is explored. An additional significant outcome of this work is the discovery and identification of kernel subsets of key states that must be present for model-learning to succeed.
Original languageEnglish
Title of host publicationComputational Discovery of Scientific Knowledge
Subtitle of host publicationIntroduction, Techniques, and Applications in Environmental and Life Sciences
EditorsSašo Džeroski, Ljupco Todorovski
Place of PublicationBerlin
PublisherSpringer
Pages248-272
Number of pages24
ISBN (Print)9783540739197
Publication statusPublished - 2007

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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Garrett, S. M., Coghill, G. M., Srinivasan, A., & King, R. D. (2007). Learning Qualitative Models of Physical and Biological Systems. In S. Džeroski, & L. Todorovski (Eds.), Computational Discovery of Scientific Knowledge: Introduction, Techniques, and Applications in Environmental and Life Sciences (pp. 248-272). (Lecture Notes in Computer Science). Berlin: Springer .