Scientific discovery: computational explorations of the creative process
Scientific discovery: computational explorations of the creative process
Data-driven approaches to empirical discovery
Artificial Intelligence
Determining repeatability and error in experimental results by a discovery system
Methodologies for intelligent systems, 5
Automated Discovery of Empirical Equations from Data
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
Automated Discovery Of Empirical Laws
Fundamenta Informaticae
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Operational definitions link scientific attributes to experimental situations, prescribing for the experimenter the actions and measurements needed to measure or control attribute values. While very important in real science, operational procedures have been neglected in machine discovery. We argue that in the preparatory stage of the empirical discovery process each operational definition must be adjusted to the experimental task at hand. This is done in the interest of error reduction and repeatability of measurements. Both small error and high repeatability are instrumental in theory formation. We demonstrate that operational procedure refinement is a discovery process that resembles the discovery of scientific laws. We demonstrate how the discovery task can be reduced to an application of the FAHRENHEIT discovery system. A new type of independent variables, the experiment refinement variables, have been introduced to make the application of FAHRENHEIT theoretically valid. This new extension to FAHRENHEIT uses simple operational procedures, as well as the system's experimentation and theory formation capabilities to collect real data in a science laboratory and to build theories of error and repeatability that are used to refine the operational procedures. We present the application of FAHRENHEIT in the context of dispensing liquids in a chemistry laboratory.