Semantic network machine for artificial intelligence computer

Abstract

A semantic network machine is applied to an artificial intelligence computer for performing inferential retrieval with respect to a knowledge base. A main associative memory stores the knowledge base consisting of knowledge data arranged to form a semantic network. Each knowledge data consists of a set of an object, an attribute, and a value. A sub associative memory is connected in parallel with the main memory and stores specific knowledge data including 'is-a' attributes of the knowledge data. In an inferential retrieval mode, when a question associated with a given object is input, an initial retrieval condition for retrieving, from the knowledge base, knowledge data necessary for answering the question is defined. While the main memory is accessed using the initial retrieval condition, the sub memory is also accessed simultaneously to read out data representing another object concept associated with the object concept included in the question by the 'is-a' attribute from the sub memory in a parallel manner. The readout data is temporarily stored in a buffer memory. When retrieval in the main memory fails, the initial retrieval condition is updated using the data stored in the buffer memory to generate a secondary initial retrieval condition supplied to the main memory, which is then successively accessed using the updated condition.