Using Classifier System with / without Genetic Algorithm in Robotics Behaviors

Abstract

A learning classifier system is one of the methods for applying a genetic-based approach to machine learning applications. An enhanced version of the system that employs the Bucket-brigade algorithm to reward individuals in a chain of co-operating rules is implemented and assigned the task of learning rules for control robotics behaviors. Illustrates the approach with the example of kicking a moving Ball into a goal (KMB). The KMB (kick a Moving Ball) System built of two-classifier subsystems work together, each classifier system learns a simple behavior, first classifier system, learn simulated robot chase behavior i.e. learn robot to move single step toward moving ball, second classifier system, learn the simulated robot approach behavior i.e. learn robot to kick the ball toward fixed goal, the system as a whole has as its learning goal the coordinate of behaviors. This work examine the performance of the simple classifier system (SCS) on the (KMB) problem, perform two SCS simulations one without the genetic algorithm enabled (GA) and one with the genetic algorithm enabled (GA) results using a classifier system with genetic algorithm show improvement over one without, and furthermore the level of performance has been high enough to rival human accuracy. Also the results without the genetic algorithm show that the apportionment of credit algorithm adjust the strength values of the rules.