Knowledge-Driven Discovery of Operational Definitions

Abstract

Knowledge representation which is internal to a computer lacks empirical meaning so that it is insufficient for the investigation of the external world. All intelligent systems, including robot-discoverers must interact with the physical world in complex, yet purposeful and accurate ways. We argue that operational definitions are necessary to provide empirical meaning of concepts, but they have been largely ignored by the research on automation of discovery and in AI. Individual operational definitions can be viewed as algorithms that operate in the real world. We explain why many operational definitions are needed for each concept and how different operational definitions of the same concept can be empirically and theoretically equivalent. We argue that all operational definitions of the same concept must form a coherent set and we define the meaning of coherence. No set of operational definitions is complete so that expanding the operational definitions is one of the key tasks in science. Among many possible expansions, only a very special few lead to a satisfactory growth of scientific knowledge. While our examples come from natural sciences, where the use of operational definitions is especially clear, operational definitions are needed for all empirical concepts. We briefly argue their role in database applications.