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
Discovering admissible simultaneous equations of large scale systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Determining Arguments of Invariant Functional Descriptions
Machine Learning
Declarative Bias in Equation Discovery
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Discovering admissible models of complex systems based on scale-types and identity constraints
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
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Conventional work on scientific discovery such as BACON derives empirical law equations from experimental data. In recent years, SDS introducing mathematical admissibility constraints has been proposed to discover first principle based law equations, and it has been further extended to discover law equations from passively observed data. Furthermore, SSF has been proposed to discover the structure of a simultaneous equation model representing an objective process through experiments. In this paper, SSF is extended to discover the structure of a simultaneous equation model from passively observed data, and is combined with the extended SDS to discover a quantitative simultaneous equation model reflecting the first principle.