Abstract
The search for structure hidden inside of visible objects has been a universal research task in Physics, Chemistry, Biology, Philosophy, and other disciplines. Discovering hidden structure is an especially challenging type of constructive induction. Not only must all the elements of hidden structure be postulated by the discoverer, but they can only be verified by indirect evidence, available at the level of observable objects. In this paper we describe a framework for the automation of hidden structure discovery. We define what is meant by hidden structure and we present a number of operators that can build models of hidden structure step by step. Our models of hidden structure consist of hidden objects of several types, admissible combinations of hidden objects, the attributes of hidden objects and their combinations, a mapping between the hidden and the observed structure, and reactions described in terms of hidden objects. We analyze the discovery of atoms, genes, and quarks to demonstrate the generality of our operators. We demonstrate how domain knowledge on the visible level is useful in operator instantiation. We discuss efficient control structures, and we define the criteria for model evaluation. Because hidden structure cannot be verified by direct observations, a successful model must pass two stages of evaluation. First, the observational consequences must be confirmed, and second, the model must be unique in its simplicity class.
Preview
Unable to display preview. Download preview PDF.
References
Fischer, P. and Źytkow, J. 1990. Discovering Quarks and Hidden Structure. Methodologies for Intelligent Systems 5, New York: North Holland, 362–370.
Harari H. 1979. Schematic Model of Quarks and Leptons. Physics Letters. B86, 83–86.
Kocabas, S. 1991. Conflict Resolution as Discovery in Particle Physics. Machine Learning 6, 277–309.
Langley, P., Simon, H.A., Bradshaw, G.L., and Źytkow, J.M. 1987. Scientific Discovery: Computational Explorations of the Creative Processes. Cambridge, MA: The MIT Press.
Lindsay, R., Buchanan, B.G., Feigenbaum, E. A. and Lederberg, R. 1980. Dendral. New York: McGraw-Hill.
Rose, D. 1989. Using Domain Knowledge to Aid Scientific Theory Revision. Proc. of the Sixth Int. Workshop on Machine Learning, Morgan Kaufmann Publ., San Mateo, CA.
Rose, D. and Langley, P. 1986. Chemical discovery as belief revision. Machine Learning, 1, 423–452.
Valdes-Perez, R.Z. 1990. Machine Discovery of Chemical Reaction Pathways. Phd. Thesis, School of Computer Science, Carnegie Mellon University.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1991 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Źytkow, J.M., Fischer, P.J. (1991). Constructing models of hidden structure. In: Ras, Z.W., Zemankova, M. (eds) Methodologies for Intelligent Systems. ISMIS 1991. Lecture Notes in Computer Science, vol 542. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-54563-8_107
Download citation
DOI: https://doi.org/10.1007/3-540-54563-8_107
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-54563-7
Online ISBN: 978-3-540-38466-3
eBook Packages: Springer Book Archive