Two theses of knowledge representation: Language restrictions, taxonomic classification, and the utility of representation services

摘要

Levesque and Brachman argue that in order to provide timely and correct responses in the most critical applications, general-purpose knowledge representation systems should restrict their languages by omitting constructs which require nonpolynomial worst-case response times for sound and complete classification. They also separate terminological and assertional knowledge, and restrict classification to purely terminological information. We demonstrate that restricting the terminological language and classifier in these ways limits these “general-purpose” facilities so severely that they are no longer generally applicable. We argue that logical soundness, completeness, and worst-case complexity are inadequate measures for evaluating the utility of representation services, and that this evaluation should employ the broader notions of utility and rationality found in decision theory. We suggest that general-purpose representation services should provide fully expressive languages, classification over relevant contingent information, “approximate” forms of classification involving defaults, and rational management of inference tools.