Modelling Experiments in Scientific Discovery
Proceedings of the Twelfth International Joint Conference on Artificial Intelligence. Learning Be Knowledge Acquisition. Pages 739-744.
Abstract
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 SlERN 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.
@inproceedings{ijcai1991p108,
title = {Modelling Experiments in Scientific Discovery},
author = {Cheng, Peter C.-H.},
booktitle = {Proceedings of the Twelfth International Joint Conference on
Artificial Intelligence, {IJCAI-91}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {John Mylopoulos and Ray Reiter},
pages = {739--744},
year = {1991},
month = {8},
note = {Learning Be Knowledge Acquisition},
url = {https://ijcai.org/proceedings/1991/108},
}
TY - CPAPER TI - Modelling Experiments in Scientific Discovery AU - Cheng, Peter C.-H. T2 - Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, IJCAI-91 PB - International Joint Conferences on Artificial Intelligence PY - 1991 SP - 739 EP - 744 UR - https://ijcai.org/proceedings/1991/108 ER -Copied
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