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.
|Title of host publication||Computational Discovery of Scientific Knowledge|
|Subtitle of host publication||Introduction, Techniques, and Applications in Environmental and Life Sciences|
|Editors||Sašo Džeroski, Ljupco Todorovski|
|Place of Publication||Berlin|
|Number of pages||24|
|Publication status||Published - 2007|
|Name||Lecture Notes in Computer Science|
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 .