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

Abstract

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.