Improving Dribbling, Passing, and Marking Actions in Soccer Simulation 2D Games Using Machine Learning

Nader Zare; Omid Amini; Aref Sayareh; Mahtab Sarvmaili; Arad Firouzkouhi; Stan Matwin; Amilcar Soares

doi:10.1007/978-3-030-98682-7_28

Improving Dribbling, Passing, and Marking Actions in Soccer Simulation 2D Games Using Machine Learning

Nader Zare
, Omid Amini
, Aref Sayareh
, Mahtab Sarvmaili
, Arad Firouzkouhi
, Stan Matwin
, Amilcar Soares

Dalhousie University
Qom University of Technology
Shiraz University
Amirkabir University of Technology
Polish Academy of Sciences
Memorial University of Newfoundland

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

The RoboCup competition was started in 1997, and is known as the oldest RoboCup league. The RoboCup 2D Soccer Simulation League is a stochastic, partially observable soccer environment in which 24 autonomous agents play on two opposing teams. In this paper, we detail the main strategies and functionalities of CYRUS, the RoboCup 2021 2D Soccer Simulation League champions. The new functionalities presented and discussed in this work are (i) Multi Action Dribble, (ii) Pass Prediction and (iii) Marking Decision. The Multi Action Dribbling strategy enabled CYRUS to succeed more often and to be safer when dribbling actions were performed during a game. The Pass Prediction enhanced our gameplay by predicting our teammate’s passing behavior, anticipating and making our agents collaborate better towards scoring goals. Finally, the Marking Decision addressed the multi-agent matching problem to improve CYRUS defensive strategy by finding an optimal solution to mark opponents’ players.

langue originale	Anglais
titre	RoboCup 2021
Sous-titre	Robot World Cup XXIV
rédacteurs en chef	Maya Cakmak, Oliver Obst
Editeur	Springer Science and Business Media Deutschland GmbH
Pages	340-351
Nombre de pages	12
ISBN (imprimé)	9783030986810
Les DOIs	https://doi.org/10.1007/978-3-030-98682-7_28
état	Publié - 1 janv. 2022
Modification externe	Oui
Evénement	24th RoboCup International Symposium, RoboCup 2021 - Virtual, Online Durée: 22 juin 2021 → 28 juin 2021

Série de publications

Nom	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13132 LNAI
ISSN (imprimé)	0302-9743
ISSN (Electronique)	1611-3349

Une conférence

Une conférence	24th RoboCup International Symposium, RoboCup 2021
La ville	Virtual, Online
période	22/06/21 → 28/06/21

Accès au document

10.1007/978-3-030-98682-7_28

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Lien vers la publication dans Scopus

Contient cette citation

Zare, N., Amini, O., Sayareh, A., Sarvmaili, M., Firouzkouhi, A., Matwin, S., & Soares, A. (2022). Improving Dribbling, Passing, and Marking Actions in Soccer Simulation 2D Games Using Machine Learning. Dans M. Cakmak, & O. Obst (eds.), RoboCup 2021: Robot World Cup XXIV (p. 340-351). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 13132 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-98682-7_28

Zare, Nader ; Amini, Omid ; Sayareh, Aref et al. / Improving Dribbling, Passing, and Marking Actions in Soccer Simulation 2D Games Using Machine Learning. RoboCup 2021: Robot World Cup XXIV. Editeur / Maya Cakmak ; Oliver Obst. Springer Science and Business Media Deutschland GmbH, 2022. p. 340-351 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{ec8b0f9bbd87448b896687ef5f0f966f,

title = "Improving Dribbling, Passing, and Marking Actions in Soccer Simulation 2D Games Using Machine Learning",

abstract = "The RoboCup competition was started in 1997, and is known as the oldest RoboCup league. The RoboCup 2D Soccer Simulation League is a stochastic, partially observable soccer environment in which 24 autonomous agents play on two opposing teams. In this paper, we detail the main strategies and functionalities of CYRUS, the RoboCup 2021 2D Soccer Simulation League champions. The new functionalities presented and discussed in this work are (i) Multi Action Dribble, (ii) Pass Prediction and (iii) Marking Decision. The Multi Action Dribbling strategy enabled CYRUS to succeed more often and to be safer when dribbling actions were performed during a game. The Pass Prediction enhanced our gameplay by predicting our teammate{\textquoteright}s passing behavior, anticipating and making our agents collaborate better towards scoring goals. Finally, the Marking Decision addressed the multi-agent matching problem to improve CYRUS defensive strategy by finding an optimal solution to mark opponents{\textquoteright} players.",

keywords = "2D soccer simulation, Dribble, Feature engineering, Multi-agent matching, RoboCup, Soccer marking",

author = "Nader Zare and Omid Amini and Aref Sayareh and Mahtab Sarvmaili and Arad Firouzkouhi and Stan Matwin and Amilcar Soares",

note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 24th RoboCup International Symposium, RoboCup 2021 ; Conference date: 22-06-2021 Through 28-06-2021",

year = "2022",

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language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Zare, N, Amini, O, Sayareh, A, Sarvmaili, M, Firouzkouhi, A, Matwin, S & Soares, A 2022, Improving Dribbling, Passing, and Marking Actions in Soccer Simulation 2D Games Using Machine Learning. Dans M Cakmak & O Obst (eds), RoboCup 2021: Robot World Cup XXIV. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), VOL. 13132 LNAI, Springer Science and Business Media Deutschland GmbH, p. 340-351, 24th RoboCup International Symposium, RoboCup 2021, Virtual, Online, 22/06/21. https://doi.org/10.1007/978-3-030-98682-7_28

Improving Dribbling, Passing, and Marking Actions in Soccer Simulation 2D Games Using Machine Learning. / Zare, Nader; Amini, Omid; Sayareh, Aref et al.
RoboCup 2021: Robot World Cup XXIV. Ed. / Maya Cakmak; Oliver Obst. Springer Science and Business Media Deutschland GmbH, 2022. p. 340-351 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 13132 LNAI).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - Improving Dribbling, Passing, and Marking Actions in Soccer Simulation 2D Games Using Machine Learning

AU - Zare, Nader

AU - Amini, Omid

AU - Sayareh, Aref

AU - Sarvmaili, Mahtab

AU - Firouzkouhi, Arad

AU - Matwin, Stan

AU - Soares, Amilcar

PY - 2022/1/1

Y1 - 2022/1/1

N2 - The RoboCup competition was started in 1997, and is known as the oldest RoboCup league. The RoboCup 2D Soccer Simulation League is a stochastic, partially observable soccer environment in which 24 autonomous agents play on two opposing teams. In this paper, we detail the main strategies and functionalities of CYRUS, the RoboCup 2021 2D Soccer Simulation League champions. The new functionalities presented and discussed in this work are (i) Multi Action Dribble, (ii) Pass Prediction and (iii) Marking Decision. The Multi Action Dribbling strategy enabled CYRUS to succeed more often and to be safer when dribbling actions were performed during a game. The Pass Prediction enhanced our gameplay by predicting our teammate’s passing behavior, anticipating and making our agents collaborate better towards scoring goals. Finally, the Marking Decision addressed the multi-agent matching problem to improve CYRUS defensive strategy by finding an optimal solution to mark opponents’ players.

AB - The RoboCup competition was started in 1997, and is known as the oldest RoboCup league. The RoboCup 2D Soccer Simulation League is a stochastic, partially observable soccer environment in which 24 autonomous agents play on two opposing teams. In this paper, we detail the main strategies and functionalities of CYRUS, the RoboCup 2021 2D Soccer Simulation League champions. The new functionalities presented and discussed in this work are (i) Multi Action Dribble, (ii) Pass Prediction and (iii) Marking Decision. The Multi Action Dribbling strategy enabled CYRUS to succeed more often and to be safer when dribbling actions were performed during a game. The Pass Prediction enhanced our gameplay by predicting our teammate’s passing behavior, anticipating and making our agents collaborate better towards scoring goals. Finally, the Marking Decision addressed the multi-agent matching problem to improve CYRUS defensive strategy by finding an optimal solution to mark opponents’ players.

KW - 2D soccer simulation

KW - Dribble

KW - Feature engineering

KW - Multi-agent matching

KW - RoboCup

KW - Soccer marking

UR - https://www.scopus.com/pages/publications/85127839601

U2 - 10.1007/978-3-030-98682-7_28

DO - 10.1007/978-3-030-98682-7_28

M3 - Conference contribution

AN - SCOPUS:85127839601

SN - 9783030986810

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 340

EP - 351

BT - RoboCup 2021

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A2 - Obst, Oliver

PB - Springer Science and Business Media Deutschland GmbH

T2 - 24th RoboCup International Symposium, RoboCup 2021

Y2 - 22 June 2021 through 28 June 2021

ER -

Zare N, Amini O, Sayareh A, Sarvmaili M, Firouzkouhi A, Matwin S et al. Improving Dribbling, Passing, and Marking Actions in Soccer Simulation 2D Games Using Machine Learning. Dans Cakmak M, Obst O, rédacteurs en chef, RoboCup 2021: Robot World Cup XXIV. Springer Science and Business Media Deutschland GmbH. 2022. p. 340-351. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-98682-7_28

Improving Dribbling, Passing, and Marking Actions in Soccer Simulation 2D Games Using Machine Learning

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