Scientific discovery: computational explorations of the creative process
Scientific discovery: computational explorations of the creative process
Discrete Mathematics - First Japan Conference on Graph Theory and Applications
Using domain knowledge to aid scientific theory revision
Proceedings of the sixth international workshop on Machine learning
Conflict Resolution as Discovery in Particle Physics
Machine Learning
Discovering quarks and hidden structure
Methodologies for intelligent systems, 5
Applications of Artificial Intelligence for Chemical Inference: The Dendral Project
Applications of Artificial Intelligence for Chemical Inference: The Dendral Project
Chemical Discovery as Belief Revision
Machine Learning
Principles of human-computer collaboration for knowledge discovery in science
Artificial Intelligence
Discovery of conservation laws via matrix search
DS'10 Proceedings of the 13th international conference on Discovery science
Automated Discovery Of Empirical Laws
Fundamenta Informaticae
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Many reported discovery systems build discrete models of hidden structure, properties, or processes in the diverse fields of biology, chemistry, and physics. We show that the search spaces underlying many well-known systems are remarkably similar when re-interpreted as search in matrix spaces. A small number of matrix types are used to represent the input data and output models. Most of the constraints can be represented as matrix constraints; most notably, conservation laws and their analogues can be represented as matrix equations. Typically, one or more matrix dimensions grow as these systems consider more complex models after simpler models fail, and we introduce a notation to express this. The novel framework of matrix-space search serves to unify previous systems and suggests how at least two of them can be integrated. Our analysis constitutes an advance toward a generalized account of model-building in science.