Neural Networks as Improving Tools for Agent Behavior
Current trends in software development show a move towards supporting autonomous, rational components (agents). One of the most interesting issues in agent technology has always been the modeling and enhancement of agent behavior. In this paper we are focused in the intersection of agent technology and machine learning techniques for producing intelligent agents. Our application shows that using neural network techniques we improve the reasoning mechanism of our agent supplying to it a new behavior which it did not possess from the beginning. The learning process can be applied initially to train ‘dummy’ agent to further improve agent reasoning. The machine learning algorithms allow for an agent to adequately respond to environment changes and improve the behavioral rules or acquire intelligent behavior. A case study will be given to demonstrate such enhancement. We simulate the behavior of a robot moving in an environment with random obstacles. Learning techniques that are added to the reasoning mechanism of this robot enrich his behavior in the dynamic environment, displaying a rational and intelligent behavior.
Keywords: Mobile robot, Intelligent Agent, Machine Learning Techniques, Neural Network, Agent Behavior
Download Full-Text
