@INPROCEEDINGS { Rsds3532,
author = {Zytkow, Jan M. and Huang, Kuang-Ming}, 
title = {Robotic Discovery: The Dilemmas of Empirical Equations}, 
booktitle = {Proceedings of the Fourth International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery}, 
conference = {International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery}, 
pages = {217-224}, 
publisher = {Japanese Research Group on Rough Sets}, 
address = {Tokyo, Japan}, 
month = {November}, 
year = 1996, 
editor = {Tsumoto, Shusaku and Kobayashi, Satoshi and Yokomori, Takashi and Tanaka, Hiroshi and Nakamura, Akira}, 
isbn = {4-947717-01-7}, 
abstract = {We describe an application of the discovery system FAHRENHEIT in mechanics. A robot arm performed the experiment similar to Galileo's experiments with the inclined plane. The discovery system has driven robot arm through many hours of experiments and used the collected data to determine empirical error and find acceptable empirical equations. FAHRENHEIT found many acceptable equations for same datasets while none for some others, creating an impasse in the BACON-like generalization process. This prompted us to develop a mechanism that selects among equalities the admissible candidates for generalization. FAHRENHEIT's final results have been evaluated in several ways. By comparing robot arm experiments and experiments conducted by hand, we demonstrated high quality of robot's data. By confronting empirical equations developed by FAHRENHEIT with theoretical models based on classical mechanics, we have shown that empirical equations provide superior fit to data. Systematic models but accounted for in empirical equations. However, it may be difficult to use empirical equations to understand the underlying process.}, 
keywords = {robotic experiments, automated discovery, empirical equations, }, 
}