A Proven Domain-Independent Scientific Function-Finding Algorithm

Programs such as Bacon, Abacus, Coper, Kepler and others are designed to find functional relationships of scientific significance in numerical data without relying on the deep domain knowledge scientists normally bring to bear in analytic work. Whether these systems actually perform as intended is an open question, however. To date, they have been supported only by anecdotal evidence-reports that a desirable answer has been found in one or more handselected and often artificial cases. In this paper, I describe a function-finding algorithm which differs radically from previous candidates in three respects. First, it concentrates rather on reliable identification of a few functional forms than on heuristic search of an infinite space of potential relations. Second, it introduces the use of distinction, significance and lack of fitthree general concepts of value in evaluating apparent functional relationships. Finally, and crucially, the algorithm has been tested prospectively on an exte...

• Type: Conference
• Year: 1990
• Where: AAAI
• Authors: Cullen Schaffer
• Keywords: AAAI 1990, Deep Domain Knowledge, Functional Relationship, Intelligent Agents, Numerical Data