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Automated Modelling of Qualitative Data Using Qualitative Reasoning and Inductive Logic Programming

The creation of qualitative models of dynamic systems is a difficult and time consuming task, requiring skills domain experts often lack. In this thesis Inductive Logic Programming (ILP) is used in order to automate the creation of these models in the Garp3 formalism. Model primitives from Garp3 are implemented as background knowledge, and it is shown ILP can learn patterns widely used by domain experts in qualitative models, as well as more complex models using these model primitives. Learning of these patterns and models is extensively tested when confronted with different types of noise. ILP shows to be very robust in coping with noisy data, and it is concluded ILP can be a substantial contribution to the automatic creation of qualitative models.