A Symbolic Manipulation Type Neural-Network Model and Knowledge Presentation with Sets

Abstract

We propose a Symbolic Manipulation type Neural-network Model (SMNM), which organizes knowledge representation with association interconnecting symbols such as semantic network, and carry out knowledge retrieval by neural networks operation. That is a new type neural networks model consisting of localized and explicit representation of knowledge, and performing intelligence as symbolic manipulater. The model has same good points of its organization, that it is ability to manipulate a lot of knowledge and to reason with semantics and flexible execution. Also the representation with network of the model can be replaced with sets of symbols on the basis of its organization, and carry out knowledge retrieval by sets operation instead of by neural networks operation. That become a new type knowledge representation with sets and will participate activity by application to programming tool for AI. This paper describes about the organization of the model, the knowledge representation by sets, and application examples with the knowledge representation.