The RoboCup agent behavior modeling challenge

Proceedings of: XI Workshop of Physical Agents 2010 in the framework of the Congreso Español De Informática, CEDI 2010. Valencia, Spain. 9th - 10th September, 2010.RoboCup is an international joint project that aims to foster Arti cial Intelligence (AI) and intelligent robotics research by providing a standard problem. RoboCup offers different challenges for intelligent agent researchers in a dynamic, real-time and multi-agent domain. One of these challenges, especially in the Simulation League, is the opponent modeling, which is crucial for the ultimate goal of the RoboCup project: develop a team of fully autonomous. In order to emphasize opponent-modeling approaches, the RoboCup Coach Competition was created and it was held every year (with some changes) from 2001 to 2006. Although there were several interesting research works about the agent modeling challenge during that time, several considerations were not well de ned and the competition was suspended after RoboCup Coach Competition 2006. In this paper, we propose a new approach for the competition to face the opponent modeling challenge in the RoboCup competition.No publicad

Using Monte Carlo Search With Data Aggregation to Improve Robot Soccer Policies

RoboCup soccer competitions are considered among the most challenging multi-robot adversarial environments, due to their high dynamism and the partial observability of the environment. In this paper we introduce a method based on a combination of Monte Carlo search and data aggregation (MCSDA) to adapt discrete-action soccer policies for a defender robot to the strategy of the opponent team. By exploiting a simple representation of the domain, a supervised learning algorithm is trained over an initial collection of data consisting of several simulations of human expert policies. Monte Carlo policy rollouts are then generated and aggregated to previous data to improve the learned policy over multiple epochs and games. The proposed approach has been extensively tested both on a soccer-dedicated simulator and on real robots. Using this method, our learning robot soccer team achieves an improvement in ball interceptions, as well as a reduction in the number of opponents' goals. Together with a better performance, an overall more efficient positioning of the whole team within the field is achieved