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].