A neural-based architecture for bridging the gap between symbolic and non-symbolic knowledge modeling

During the last decade many research efforts have been directed towards studying the relative merits of the symbolic (rooted in logic, easily understandable) and non-symbolic (numeric, difficult to understand) Artificial Intelligence (AI). Specifically, efforts have been directed towards discovering techniques to translate between knowledge available in one format to another; such as between Fuzzy Rule-based Systems (FRS) and Artificial Neural Networks (ANNs); combining both formats in a single hybrid system; such as Adaptive Neuro-Fuzzy Systems (ANFIS); or even equating both of them by introducing a new fuzzy logic operator [1]. The present paper proposes a new framework; based on a modification of the work given in [1]; that has several advantages over pure FRS, pure ANN systems and existing hybrid approaches. It is capable of producing meaningful plausible rules whether prior expert's knowledge is available or not. The theoretical foundation of this framework, as well as its application to a robot obstacle avoidance case study are discussed. Its suitability for the solution of general optimization problems is highlighted in [14].