Donald Bren School of Information and Computer Sciences

In recent years, researchers in machine learning have investigated three main aspects of empirical discovery: forming taxonomies, finding qualitative laws, and finding quantitative laws. In this paper, we introduce IDS (Integrated Discovery System), a system that integrates these aspects of scientific discovery. We begin by examining the system's representation, showing how it describes events like chemical reactions as sequences of qualitative states. IDS incrementally processes these sequences to build a hierarchy of states, forming qualitative laws and numeric relations to augment this hierarchy. We discuss the three learning components that produce this behavior, using examples to illustrate the processes. Since this work is still in progress, we describe the current status of IDS and close with our plans for extending the system.