Evaluating representational systems in artificial intelligence

摘要

All artificial reasoners work within representational systems. These systems, which may have varying levels of formality or detail, determine the space of possible representations over which the artificial reasoner can operate, by defining the syntactic and semantic properties of the symbols, structures, and inferences that they manipulate. But we are now seeing an increasing need for the ability to reason over representational systems, rather than just working within them. A prerequisite of performing such reasoning is the ability to evaluate and compare representational objects (and to know the difference between them). We survey the criteria that are used for such evaluations in AI, machine learning, and other AI-related fields. To aid our survey, we introduce a formalism of representations, representational systems, and representational spaces that lends itself nicely to an analysis of the criteria typically used for evaluating them.