📜 Abstract: This team description paper describes the direction and methods of team optimization
over the past year at YuShan2024. There are two main parts of the work, which are the analysis and
optimization of the shooting module and the analysis of physical strength and running distance. In
a lot of tests in the past, we found that the team has the situation that even though the player is in
front of the goal and has a chance to shoot, but there is no shooting action, and missed some good
chances, so we hope to find the main direction to optimize the shooting module by analyzing the
shooting data from multiple perspectives. At the same time, in the test with some strong teams, we
found that YuShan's stamina maintenance has a big problem, often around 500 cycles before the
end of the half, the player's stamina has been exhausted, resulting in the defense out of position, so
we hope that through the method of data analysis, we can find the difference in the stamina
maintenance of the players between us and the strongest teams, so as to further make optimization
of the team's stamina maintenanc.
📄 Title: AEteam Soccer Simulation 2D Team Description Paper 2024
✍️ Authors: Erfan Fathi , Soroush Mazloom , Parham Keyhani , Amir hosein Nikfetrat and Vahid khodabakhshi
📜 Abstract: This team description paper presents an overview of previous work
and recent research topics of Team AEteam. In this article, we aim to enhance
our understanding and improve the efficiency of offensive players escaping by
exploring and expanding our knowledge on the subject. To achieve this, we em-
ploy the mentioned artificial intelligence methods with the hope of improving the
trend of players’ attacks.
📜 Abstract: In the Soccer Simulation 2D environment, accurate observa-
tion is crucial for effective decision-making. However, challenges such as
partial observation and noisy data can hinder performance. To address
these issues, we propose a denoising algorithm that leverages predictive
modeling and intersection analysis to enhance the accuracy of observa-
tions. Our approach aims to mitigate the impact of noise and partial
data, leading to improved gameplay performance. This paper presents
the framework, implementation, and preliminary results of our algorithm,
demonstrating its potential in refining observations in Soccer Simulation
2D. Cyrus 2D Team is using a combination of Helios, Gliders, and Cyrus
base codes.
📜 Abstract: This team description paper presents an overview of previous
work and recent research topics of Team HELIOS2024. This year, we en-
hanced the online opponent formation identification, which our team had
previously implemented. The primary contribution involves accelerating
computations through the introduction of tile coding.
This paper describes one of our recent efforts, the enhancement of oppo-
nent identification.
📄 Title: MARS Soccer Simulation 2D Team Description Paper 2024
✍️ Authors: Mehdi torshani , Mohammad Maziani , Mohammad Ghasemi , Mohammad
Bonakdar Tehrani , Fakhteh Hosseinkhani , Mahdieh GhasemPour , Afsaneh Imani ,
Zahra Samadi ,and Mercedeh Ghasemi
📜 Abstract: Report examines the records and performance of the Mars team in the
two-dimensional football simulation game.The Mars team began its activities in
robotics in 2006. This team has developed various algorithms in the ation league
using the Agent2D base code. In this report, we will discuss part of this team's
goals in decision-making methods and the player learning system. The algorithm
discussed in this report is a decision tree that makes the best decision for each
player using the highest probability and the best training data.
✍️ Authors: Sebastian Marian, Dorin Luca, Radu Sacuiu, Bogdan Sarac, Ovidiu Cotarlea
📜 Abstract: Oxsy team has been founded in July 2002 as a graduation project of
one student, Sebastian Marian, in the field of Multi-Agent Systems [1] Lucian
Blaga University (Sibiu - Romania). After graduation he continued the work on
this project and eventually Oxsy team was born. As we started from scratch [2]
our ideas, concepts and beliefs, have been implemented year by year, and today,
we are happy to see our evolution, as our team was growing in these years,
much more than we have expected from the beginning. If we will qualify for
this year competition, we will reach at the 20th consecutive participation, in
RoboCup [3] Soccer Simulation League.
📄 Title: RobˆoCIn Soccer Simulation 2D
Team Description Paper for RoboCup 2024
✍️ Authors: Bonna Borsoi, Felipe Pereira, Gabriel Souza, Heitor Souza, Jeferson Ara ́ujo,
Maria L. Silva, Mariana Coelho, Mateus Machado, Mateus Soares, Pedro V.
Cunha, Rafael Labio, Walber M. Rodrigues, and Edna Barros
📜 Abstract: RobˆoCIn Soccer Simulation 2D team, based at the Universi-
dade Federal de Pernambuco, was founded in 2018. In our debut compe-
tition at the Latin American Robotics Competition (LARC) in Jo ̃ao Pes-
soa, Para ́ıba, Brazil, we secured fourth place against other Latin Amer-
ican teams. The following year, we competed in the RoboCup for the
first time and achieved a ninth-place finish. In 2023, we placed in the
6th position on RoboCup and first place at LARC. In this paper, we
present the work developed over the past year, especially the refinement
and improvement of the agent’s behaviors and changes made to the field
evaluator.
✍️ Authors: Thomas Gabel, Berkan Eren, Eicke Godehardt
📜 Abstract: The main focus of FRA-UNIted’s effort in the RoboCup soc-
cer simulation 2D domain is to develop and to apply machine learning
techniques in complex domains. In particular, we are interested in ap-
plying reinforcement learning methods, where the training signal is only
given in terms of success or failure. In this paper, we review some of our
recent efforts taken during the past year, putting a special focus on tool
support and team performance analyses.
📄 Title: ITAndroids 2D Soccer Simulation
Team Description Paper 2024
✍️ Authors: Davi M. Vasconcelos, Luiz F. B. Ramos, Marcos R. O. A. Maximo, Nean
Segura, and Vinícius F. Almeida
📜 Abstract: The ITAndroids 2D Soccer Simulation team is composed of
undergraduate students of the Aeronautics Institute of Technology. This
paper explores three topics: defensive behavior for 1-versus-1 situations
with Deep Reinforcement Learning, improvement of the goalkeeper’s po-
sitioning, and influence of the librcsc’s version on the agent2d perfor-
mance. The developed defensive behavior outperformed agent2d in the
presented task. The novel goalkeeper’s implementation dramatically de-
creased the loss rate of ITAndroids against RoboCIn. Furthermore, the
agent2d performance considerably depended on the librcsc’s version: the
latest agent2d running with the most updated librcsc release achieved a
win rate of 71.6% against agent2d 3.1.0 with librcsc 4.1.0.
📄 Title: R2D2 Soccer Simulation 2D Team Description Paper
Robocup World Cup 2024
✍️ Authors: Seyed Hassan Majid Zonouzi, Mohammad Hesam Nasiri, Sanaz Moosapour, Sina
Taheri Behrooz, Sana Mousavi, Seyed Mostafa Atyabi, Fatemeh Houra Haghighatkhah
📜 Abstract: The paper details the R2D2 team’s (R3CESBU) algorithm and de-
velopment clarifications for RoboCup's 2D soccer simulation league. It intro-
duces goalie, shooting, and marking advancements, emphasizing the ranking al-
gorithm for chain action states and dynamic positioning. R2D2 employs artificial
intelligence techniques, combining behavioral cloning and game log parsing to
enhance kickable agents and goalies. The research contributes to the evolution of
competitive play in the RoboCup 2D Simulation League. The offence tactics in-
volve improvements in the field evaluator, through-pass, unmarking, and shoot-
ing algorithms, resulting in significant performance enhancements.