An Efficient Kick Strategy for Agents in the 2D Simulation League

Abstract

This paper aims to answer the following research question: "How to build an efficient kick strategy for agents in the 2D Simulation League?". The robot soccer provides an opportunity for students and professionals to apply their concepts of intelligent agent development. One of the main challenges of this game is to decide when a player must kick the ball to the goal. The proposed solution to solve this question is a data mining approach. The solution consists of three components: 1) use of the Random Forest technique as a classifier, 2) enrichment of the database through the construction of new variables and 3) Features Selection. In order to validate the proposed solution, a comparative study between the original kick strategy of a base team and the solution proposed was conducted. Experiments showed that the proposed approach delivers a performance superior. The results showed that the proposed policy reached a winning rate of 65% against 28% of the original.