Learning Qualitative Models of Physical and Biological Systems

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.