Integration of constraint logic programming and artificial neural networks for driving robots

We propose a robot architecture to integrate symbolic and non-symbolic information processings. Artificial neural networks (ANN) are quick, flexible and robust. Symbolic processing is on the other hand comprehensible, effective, controllable, and consistent. To integrate symbolic and non-symbolic methods, we consider the relation between a robot and its environment as constraints. To describe and solve such constraints we turn to constraint logic programming (CLP). To construct a robot that works in the complex environment, CLP and ANN are integrated into a unified framework such that CLP evaluates the behavior candidates proposed by ANN according to the constraints and ANN learns adequate behavior according to evaluations by CLP. We implemented the decision process in our robot that drove through a test course as we expected.