Measuring and Improving the Effectiveness of Representations

Abstract

This report discusses what it means to claim that a representation is an effective encoding of knowledge. We first present dimensions of merit for evaluating representations, based on the view that usefulness is a behavioral property, and is necessarily relative to a specified task. We then provide methods (based on results from mathematical statistics) for reliably measuring effectiveness empirically, and hence for comparing different representations. We also discuss weak but guaranteed methods of improving inadequate representations. Our results are an application of the ideas of formal learning theory to concrete knowledge representation formalisms. 1 Introduction A principal aim of research in knowledge representation and reasoning is to design good formalisms for representing knowledge about the world. This paper gives operational criteria for evaluating the goodness of a "representation". 1 Many areas of AI research can use these results. For example, many papers on nonmonoton...