RoboCup 2D

RoboCup was created in 1996 by a group of Japanese, American, and European Artificial Intelligence and Robotics researchers with a formidable, visionary longterm
challenge: “By 2050 a team of robot soccer players will beat the human World Cup champion team.” At that time, in
the mid 90s, when there were very few effective mobile robots and the Honda P2 humanoid robot was presented to a stunning public for the first time also in 1996, the RoboCup challenge,
set as an adversarial game between teams of autonomous robots, was fascinating and exciting.
RoboCup enthusiastically and concretely introduced three robot soccer leagues, namely “Simulation,” “SmallSize,”
and “MiddleSize,” as we explain below, and organized its first
competitions at IJCAI’97 in Nagoya with a surprising number of 100 participants [RC97]. It was the beginning of what became a continously growing research community. RoboCup established
itself as a structured organization, the RoboCup Federation www.RoboCup.org, fostering annual competition events, where the scientific challenges faced by the researchers are addressed in a setting that is attractive also to the general public. The RoboCup events have become the most popular and attended in the research fields of AI and robotics, including also a technical symposium with contributions relevant to the RoboCup competitions and beyond to the general AI and robotics.

This article presents the UaiSoccer2D team, a simulation
team of robots soccer simulated at UFSJ - Federal University of São João del-Rei, MG, Brazil. Will be present a modeling strategy for use of reinforcement learning algorithm Q-learning.

This article presents the UFSJ2D Team, a team of simulated
robots soccer from UFSJ - Federal University of Sao Joao del-Rei, MG, Brazil. The main goals in this paper are to apply reinforcement learning in the optimization of decision taking and to model a strategy using fuzzy logic.