Default reasoning in semantic networks: A formalization of recognition and inheritance

摘要

Many knowledge-based systems express domain knowledge in terms of a hierarchy of concepts (or frames)—where each concept is a collection of property value (or slot filler) pairs. One can associate two interesting classes of inference with such information structures: inheritance and recognition. Attempts at formalizing inheritance and recognition, however, have been confounded by the presence of conflicting property values among related concepts. Such conflicting information gives rise to the problems of exceptions and multiple inheritance during inheritance, and partial matching during recognition. This paper presents a formalization of inheritance and recognition based on the principle of maximum entropy. The proposed formalization offers several advantages: it admits necessary as well as default property values, it deals with conflicting information in a principled manner, and it solves the problems of exceptions, multiple inheritance, as well as partial matching. It can also be shown that the proposed formalization may be realized as a massively parallel network of simple processing elements that can solve an interesting class of inheritance and recognition problems extremely fast—in time proportional to the depth of the conceptual hierarchy.