The Computational Support of Scientific Discovery
Machine Learning and Its Applications, Advanced Lectures
The Computer-Aided Discovery of Scientific Knowledge
DS '98 Proceedings of the First International Conference on Discovery Science
Automated Formulation of Reactions and Pathways in Nuclear Astrophysics: New Results
DS '01 Proceedings of the 4th International Conference on Discovery Science
Knowledge discovery in databases: the purpose, necessity, and challenges
Handbook of data mining and knowledge discovery
Automated scientific discovery
Handbook of data mining and knowledge discovery
Automated discovery in a chemistry laboratory
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Scientific model-building as search in matrix spaces
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Mathematical and Computer Modelling: An International Journal
Hi-index | 0.00 |
One of the major goals of 18th century chemistry was to determine the components of substances. In this paper we describe STAHL, a system that models significant portions of 18th century reasoning about compositional models. The system includes a number of heuristics for generating componential models from reactions, as well as error recovery mechanisms for dealing with inconsistent results. STAHL processes chemical reactions incrementally, and is therefore capable of reconstructing extended historic episodes, such as the century-long development of the phlogiston theory. We evaluate STAHL's heuristics in the light of historical data, and conclude that the same reasoning mechanisms account for a variety of historical achievements, including Black's models of mild alkali and Lavoisier's oxygen theory. STAHL explains the generation of competing accounts of the same reactions, since the system's reasoning chain depends on knowledge it has accumulated at earlier stages.