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
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Is there any need for domain-dependent control information? a reply
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Discovering Admissible Simultaneous Equation Models from Observed Data
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Toward the Discovery of First Principle Based Scientific Law Equations
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
Communicable Knowledge in Automated System Identification
Computational Discovery of Scientific Knowledge
Communicability Criteria of Law Equations Discovery
Computational Discovery of Scientific Knowledge
Bridging control and artificial intelligence theories for diagnosis: A survey
Engineering Applications of Artificial Intelligence
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Most conventional law equation discovery systems such as BACON require experimental environments to acquire their necessary data. The mathematical techniques such as linear system identification and neural network fitting presume the classes of equations to model given observed data sets. The study reported in this paper proposes a novel method to discover an admissible model equation from a given set of observed data, while the equation is ensured to reflect first principles governing the objective system. The power of the proposed method comes from the use of the scale-types of the observed quantities, a mathematical property of identity and quasi-bi-variate fitting to the given data set. Its principles and algorithm are described with moderately complex examples, and its practicality is demonstrated through a real application to psychological and sociological law equation discovery.