In RoboCup 2004 we participated in “classical” 2DSimulation League as well as in the new introduced and first time hold 3D-competition. Our team “AT Humboldt” placed 10th while “AT Humboldt 3D” became vice world champion. Like in the past years we used our soccer agents as a research testbed for long-term deliberation and realtime reasoning, cooperation and coordination in MAS, CBR-aided decisi...

In 2D Soccer Simulation league, agents will decide based on information and data in their model. Effective decisions need to have world model information without any noise and missing data; however, there are few solutions to omit noise in world model data; so we should find efficient ways to reduce the effect of noise when making decisions. In this article we evaluate some simple solutions whe...

RoboCup is an international scientific robot competition in which teams of multiple robots compete against each other. Its different leagues provide many sources of robotics data, that can be used for further analysis and application of machine learning. This paper describes a large dataset from games of some of the top teams (from 2016 and 2017) in RoboCup Soccer Simulation League (2D), where ...

The RoboCup 2D simulation domain has served as a platform for research in AI, machine learning, and multiagent systems for more than two decades. However, for the researcher looking to quickly prototype and evaluate different algorithms, the full RoboCup task presents a cumbersome prospect, as it can take several weeks to set up the desired testing environment. The complexity owes in part to th...

From the RoboCup Website: “The Robot World Cup Initiative (RoboCup) is an attempt to foster AI and intelligent robotics research by providing a standard problem where wide range of technologies can be integrated and examined. For this purpose, RoboCup chose to use soccer game, and organize RoboCup: The Robot World Cup Soccer Games and Conferences.” The aim of the paper is provide an AI research...

In this paper, we describe the soccer playing agent developed by our team UT Austin Villa to participate in the RoboCup 3D simulation soccer competition held in Atlanta, U.S.A., in July 2007. The 2007 competition saw the introduction of a humanoid robot in the 3D simulation league. We present an overview of this new version of the simulation league, followed by a description of our approaches i...

Increasingly, multi-agent systems are being designed for a variety of complex, dynamic domains. EEective agent interactions in such domains raise some of the most fundamental research challenges for agent-based systems, in teamwork, multi-agent learning and agent modelling. The RoboCup research initiative, particularly the simulation league, has been proposed to pursue such multi-agent research...

Multi-agent systems are broadly known for being able to simulate real-life situations which require the interaction and cooperation of individuals. Opponent modeling can be used along with multi-agent systems to model complex situations such as competitions like soccer games. In this paper, a model for predicting opponent moves is presented. The model is based around an offline step (learning p...