Scientific discovery: computational explorations of the creative process
Scientific discovery: computational explorations of the creative process
Determining Arguments of Invariant Functional Descriptions
Machine Learning
Modelling scientific discovery (experiment theory, research program)
Modelling scientific discovery (experiment theory, research program)
Automatic stimulation of experiments and learning based on prediction failure recognition
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
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Investigating the character of scientific discovery using computational models is a growing area in Artificial Intelligence and Cognitive Science. Scientific discovery involves both theory and experiments, but existing discovery systems have mainly considered the formation and modification of theories. This paper focuses on the modelling of experiments. A general characterization of the nature of experiments is given and more specifically Galileo's motion experiments are examined. The STERN scientific discovery system has been used to model Galileo's investigations of free fall, and is introduced here. The system has an extensive representation for experiments and uses experiments to: (i) confirm existing hypotheses; (ii) find new hypotheses; (ii) enhance its own performance; and, (iv) make intractable hypotheses tractable.