A Prolog-like inference system based on Neural Logic - An attempt towards fuzzy Neural Logic programming

Research under the name of Neural Logic Networks is an attempt to integrate connectionist models and logic reasoning [8, 9]. With a Neural Logic Network, a simple neural network structure with suitable weight(s) can be used to represent a set of flexible operations, which offer increased possibilities in dealing with inference in real-world problem solving. They also possess useful properties in an extended logic system which is called Neural Logic. One of the important features of Neural Logic is that all its operations can be defined and realized by neural networks, which form Neural Logic Networks. As one part of the research on Neural Logic Networks, fuzzy neural logic programming has been proposed [6]. This paper introduces a Prolog-like inference system based on Neural Logic as an implementation of fuzzy neural logic programming. In this system, fuzzy reasoning is executed by the Neural Logic inference engine with incomplete or uncertain knowledge. The framework of the system and its inference mechanism are described.