Title

Sinbad Automation Of Scientific Discovery: From Factor Analysis To Theory Synthesis

Keywords

Bayesian networks; Blind source separation; Causal relations; Concept acquisition; Curse of dimensionality; IMAX; Knowledge representation; nonlinear factor analysis; Virtual Scientist

Abstract

Modern science is turning to progressively more complex and data-rich subjects, which challenges the existing methods of data analysis and interpretation. Consequently, there is a pressing need for development of ever more powerful methods of extracting order from complex data and for automation of all steps of the scientific process. Virtual Scientist is a set of computational procedures that automate the method of inductive inference to derive a theory from observational data dominated by nonlinear regularities. The procedures utilize SINBAD - a novel computational method of nonlinear factor analysis that is based on the principle of maximization of mutual information among non-overlapping sources, yielding higher-order features of the data that reveal hidden causal factors controlling the observed phenomena. The procedures build a theory of the studied subject by finding inferentially useful hidden factors, learning interdependencies among its variables, reconstructing its functional organization, and describing it by a concise graph of inferential relations among its variables. The graph is a quantitative model of the studied subject, capable of performing elaborate deductive inferences and explaining behaviors of the observed variables by behaviors of other such variables and discovered hidden factors. The set of Virtual Scientist procedures is a powerful analytical and theory-building tool designed to be used in research of complex scientific problems characterized by multivariate and nonlinear relations. © 2004 Kluwer Academic Publishers.

Publication Date

9-6-2004

Publication Title

Natural Computing

Volume

3

Issue

2

Number of Pages

207-233

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1023/B:NACO.0000027756.50327.26

Socpus ID

4344705639 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/4344705639

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