Experimenting and theorizing in theory formation
ISMIS '86 Proceedings of the ACM SIGART international symposium on Methodologies for intelligent systems
Scientific discovery: computational explorations of the creative process
Scientific discovery: computational explorations of the creative process
Data-driven approaches to empirical discovery
Artificial Intelligence
Three facets of scientific discovery
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
An artificial experimenter for enzymatic response characterisation
DS'10 Proceedings of the 13th international conference on Discovery science
Learning process models with missing data
ECML'06 Proceedings of the 17th European conference on Machine Learning
Automated Discovery Of Empirical Laws
Fundamenta Informaticae
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We describe an application of the discovery system FAHRENHEIT in a chemistry laboratory. Our emphasis is on automation of the discovery process as oposed to human intervention and on computer control over real experiments and data collection as opposed to the use of simulation. FAHRENHEIT performs automatically many cycles of experimentation, data collection and theory formation. We report on electrochemistry experiments of several hour duration, in which FAHRENHEIT has developed empirical equations (quantitative regularities) equivalent to those developed by an analytical chemist working on the same problem. The theoretical capabilities of FAHRENHEIT have been expanded, allowing the system to find maxima in a dataset, evaluate error for all concepts, and determine reproducibility of results. After minor adjustments FAHRENHEIT has been able to discover regularities in maxima locations and heights, and to analyse repeatability of measurements by the same mechanism, adapted from BACON, by which all numerical regularities are detected.