Using Qualitative Models to Guide Inductive Learning

This paper presents a method for using qualitative models to guide inductive learning. Our objectives are to induce rules which are not only accurate but also explainable with respect to the qualitative model, and to reduce learning time by exploiting domain knowledge in the learning process. Such explainability is essential both for practical application of inductive technology, and for integrating the results of learning back into an existing knowledge-base. We apply this method to two process control problems, a water tank network and an ore grinding process used in the mining industry. Surprisingly, in addition to achieving explainability the classificational accuracy of the induced rules is also increased. We show how the value of the qualitative models can be quantified in terms of their equivalence to additional training examples,and finally discuss possible extensions.Cet article d\ue9crit un projet de d\ue9veloppement d'un prototype de syst\ue8me expert op\ue9rationnel, fait en collaboration, qui aidera les op\ue9rateurs et les techniciens de l'usine \ue0 assurer le d\ue9pannage et \ue0 optimiser la partie vapeur et condensat du train de s\ue9chage de la machine \ue0 papier. On y pr\ue9sente les objectifs des organisations qui collaborent au projet et la fonctionnalit\ue9 du prototype et on aborde le cycle de vie ainsi que les perspectives de d\ue9veloppement ult\ue9rieur de ce prototype.NRC publication: Ye