A model-driven approach to scientific law discovery
This thesis presents a structural model of one aspect of science, the theory-driven discovery of empirical laws, in terms of the knowledge structures and reasoning processes that it involves; and describes a machine learning system designed to embody the major features of the model, called OZ, which is designed to investigate the transport properties of an unknown membrane separating two solutions. Inductive data-driven discovery is an important process in science, but takes place within very tightly constrained limits defined by theoretical reasoning. An explicit specification of the possible search space for a law is a law framework; this takes the form of a law with some undetermined parameters. Inductive law discovery is the search for the values of these free parameters. According to the model informal qualitative models (IQMs) describing the essential structural features of a physical system are used to guide the selection of appropriate variables for scientific law discovery, and the selection of an appropriate mathematical function for a law. Our analysis differs from previous work in machine discovery in stressing the importance of models of internal structure in scientific discovery. OZ comprises a domain independent control structure and a set of domain independent procedures, plus a set of domain dependent heuristics for the membrane properties domain. It constructs a set of candidate IQMs for the unknown membrane, and designs goal-directed experiments to determine which IQM is the right one, generating and testing qualitative predictions about the patterns to be expected in numerical data. When it has identified a single model as correct, it constructs law frameworks for possible laws describing the transport properties of the membrane, then designs different experiments to gather data to supply to an inductive law discovery function, which looks for a law of the type specified by each law framework.