Building a Belief-Desire-Intention Agent for Modeling Neural Networks

This article presents an innovative learning technique for modeling nonlinear systems. Our belief-desire-intention algorithm for neural networks can effectively identify the parameters of most relevance to a model for the online adjustment of weights, neurons, and layers. We present a detailed explanation of each component in the proposed agent, and successfully apply our model to describe the lateral forces on a tire under a range of test conditions. The model output is compared to test data and the output of an existing neural network model. Our results demonstrate that the belief-desire-intention agent is reliable and applicable in nonlinear modeling and is superior to backpropagation neural networks.