TY - GEN
T1 - Learning qualitative metabolic models
AU - Coghill, George MacLeod
AU - Garrett, S. M.
AU - King, R. D.
PY - 2004/11
Y1 - 2004/11
N2 - 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, QOPH learns the structural relationships between the observed variables, known to be a hard problem. QOPH has been shown capable of learning models with hidden (unmeasured) variables, under different levels of noise, and from qualitative or quantitative input data.
AB - 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, QOPH learns the structural relationships between the observed variables, known to be a hard problem. QOPH has been shown capable of learning models with hidden (unmeasured) variables, under different levels of noise, and from qualitative or quantitative input data.
M3 - Published conference contribution
SN - 1586034529
SN - 978-1586034528
T3 - Frontiers in Artificial Intelligence and Applications
SP - 445
EP - 449
BT - ECAI 2004: 16th European Conference on Artificial Intelligence, Proceedings
A2 - Lopez de Mantaras, Ramon
A2 - Saitta, L.
PB - IOS Press
CY - Amsterdam, Netherlands
T2 - 16th European conference on Artificial Intelligence
Y2 - 22 August 2004 through 27 August 2004
ER -