Evaluating Feature Selection Techniques in Simulated Soccer Multi Agents System

Since the quality of data affects the success rate of data mining and learning algorithms, it is always attempted to identify and remove the irrelevant and redundant information in a dataset. Robotic soccer is a multi-agent system in which agents play in real-time, dynamic, complex and noisy environment. Many parameters affect the result of shooting toward the goal and agents must response to variations in soccer field rapidly. Therefore it is impossible to use all features in scoring behavior. This paper selects dataset for effective features of scoring behavior simulated soccer agents, then compares the size of the trees and accuracy produced by each feature selection scheme against the size of the trees and accuracy produced by C4.5 with no feature selection method. Experimental results have shown that dimensionality reductions lead to operate faster and more effective learning algorithm in real-time simulated soccer agent.