ECAI 2004 Conference Paper

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Learning qualitative metabolic models

George M. Coghill, Simon M. Garrett, Ross D. King

The ability to learn a model of a system from observations of the system and background knowledge is central to intelligence, and the automation of the process is a key research goal of Artificial Intelligence. We present a model-learning system, developed for application to scientific discovery problems, where the models are scientific hypotheses and the observations are experiments. The learning system, {\sc Qoph} learns the {\it structural} relationships between the observed variables, known to be a hard problem. {\sc Qoph} has been shown capable of learning models with hidden (unmeasured) variables, under different levels of noise, and from qualitative or quantitative input data.

Keywords: Model learning, Qualtitative Reasoning, ILP, Computational Biology

Citation: George M. Coghill, Simon M. Garrett, Ross D. King: Learning qualitative metabolic models. In R.López de Mántaras and L.Saitta (eds.): ECAI2004, Proceedings of the 16th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2004, pp.445-449.

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ECAI-2004 is organised by the European Coordinating Committee for Artificial Intelligence (ECCAI) and hosted by the Universitat Politècnica de València on behalf of Asociación Española de Inteligencia Artificial (AEPIA) and Associació Catalana d'Intel-ligència Artificial (ACIA).