Discrimination of semi-quantitative models by experiment selection : Method and application in population biology.
I. Vatcheva, O. Bernard, H. de Jong, J.-L. Gouzé, N. Mars.
Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, IJCAI-01, B. Nebel (ed.), Morgan Kaufmann, San Mateo, CA, 74-79, 2001.
Modeling an experimental system often results in a number of alternative models that are justified equally well by the experimental data. In order to discriminate between these models, additional experiments are needed. We present a method for the discrimination of models in the form of semi-quantitative differential equations. The method is based on an entropy criterion for the selection of the most informative experiment. The applicability of the method is demonstrated on a real-life example, the discrimination of a set of competing models of the growth of phytoplankton in a bioreactor.