AGENT BASED MODELLING FOR NEW TECHNIQUE IN NEURO SYMBOLIC INTEGRATION

Logic program and neural networks are two important aspects in artificial intelligence. This paper is part of an endeavour towards neural networks and logic programming integration. The goal in performing logic programming based on the energy minimization scheme is to achieve the best ratio of global minimum. However, there is no guarantee to find the best minimum in the network. To achieve this, a new learning rule based Activation Function was derived to accelerate the performance of doing logic programming in Hopfield Neural Network (HNN). This paper also shows focused on agent based modelling for presenting performance of doing logic programming in Hopfield network using new activation function. The effects of the activation function are analyzed mathematically and compared with the existing method. Computer simulations are carried out by using NETLOGO to validate the effectiveness on the new activation function. The resuls obtained showed that the new activation function outperform the existing method in doing logic programming in Hopfield network. The models developed by agent based modelling also support this theory.