An Hybrid Neural System based on Adaptive Resonance Theory and Representational Redescription capable of Variable Binding

In this work we propose a hybrid neural architecture named VaBiARRT based on the adaptive resonance theory that relies on representational redescription to archive a high degree of generalization and code compactness. Knowledge is autonomously structured as a hierarchical topology of fuzzy input classes and how these classes are related with the outputs. A two-way abstraction/particularization process takes place in order to rewrite the established relations to make them as abstract as possible without loosing accuracy. The internal handling of the representation of knowledge can be interpreted as a standard pattern matching variable binding process. Besides providing a formal description of VaBiARRT we also solve a sample problem related to the representational redescription hypothesis in order to study the knowledge redescription process and the performance of the network.