Data-Driven Approaches to Empirical Discovery
Abstract
In the last decade a few artificial intelligence researchers have turned their attention to a domain often considered the realm of genius - scientific discovery. The vast majority of this work has focused on empirical discovery, and much of the effort has been concerned with the discovery of numeric laws. This paper traces one evolutionary chain of research on discovery in particular the development of data-driven heuristic methods relating to numeric discovery. The authors examine four systems - Gerwin's function induction system, Langley, Bradshaw, and Simon's BACON, Zytkow's FAHRENHEIT, and Nordhausen and Langley's IDS - and describe how each program introduces abilities lacking in earlier systems. The conceptual advances involve three different but interrelated aspects of discovery: the form of laws and theoretical terms discovered; the ability to determine the scope and context of laws; and the ability to design experiments. This document evaluates each of the systems, but focuses on their theoretical contributions rather than on reporting their behavior in specific domains. It closes the paper by reviewing the work on machine discovery from the views of the history and philosophy of science. (KR)
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 31, 1988
- Accession Number
- ADA201850
Entities
People
- Jan M. Zytkow
- Pat Langley
Organizations
- University of California, Irvine