An Integrated Approach to Empirical Discovery
Abstract
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. Keywords: Machine learning, Conceptual clustering, Taxonomy formation, Qualitative discovery, Quantitative discovery, Empirical laws. (
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 01, 1989
- Accession Number
- ADA212175
Entities
People
- Bernd Nordhausen
- Pat Langley
Organizations
- University of California, Irvine