Author(s)

Stephan Rudolph

Abstract

In the field of case-based reasoning in artificial intelligence, the general derivation of so-called similarity measures is still an unresolved open question. In this work the theoretical framework of dimensional analysis is used to derive appropriate similarity measures for a case-based reasoning technique. For the subclass of all case descriptions in engineering and physics consisting of real-valued quantities with physical units, it is shown how the Pi-Theorem of Buckingham can be used to construct similarity measures from these case descriptions. The necessary functional model assumptions are defined and the theoretical foundations are discussed. Within this functional model approach a

Keywords