A Computational Theory of Discovery

University Of Maryland, College Park, College Park MD

Investigators

Abstract

he models used in inductive inference have their roots in the models used by the philosophers of science who were discussing the scientific method. The goal there, and in prior work in learning theory, was to come up with an explanation of the phenomenon under consideration. However, scientists rarely work directly for the grand goal of a complete explanation. The more modest goal of finding features and facts about the observed data is pursued. A variety of types of algorithms that could be construed as discovering their final result will be investigated. We propose to consider computations that discover rather than compute their intended result. A logic of discovery will be developed and investigated. This study is particularly relevant to contemporary science as automated data generation techniques produce sufficient volumes of data to overwhelm the analysis abilities of humans. The goal of our work is to illuminate precisely what can and cannot be accomplished by automatic data analysis algorithms. Such algorithms are used in data mining and text analysis for world wide web search engines.