Neuro-symbolic Reasoning System for Modeling Complex Behaviours.

Abstract

A neuro-symbolic reasoning strategy for modelling a complex system is presented in which the aim is to forecast, in real time, the physical parameter values of a dynamic environment: the ocean. In situations in which the rules that determine a system are unknown the prediction of the parameter values that determine the characteristic behaviour of the system can be a problematic task. In such a situation it has been found that a case-based reasoning system, in combination with an artifical neural network, can provide a more effective means of performing such predictions than other connectionist or symbolic techniques. The case-based reasoning system incorporates a radial basis function artificial neural network for the case adaptation. The results obtained from experiments, in which the system operated in real time in the oceanographic environment, are presented.