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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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’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.

Original language	English
Title of host publication	RoboCup 2021
Subtitle of host publication	Robot World Cup XXIV
Editors	Maya Cakmak, Oliver Obst
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	340-351
Number of pages	12
ISBN (Print)	9783030986810
DOIs	https://doi.org/10.1007/978-3-030-98682-7_28
Publication status	Published - 1 Jan 2022
Externally published	Yes
Event	24th RoboCup International Symposium, RoboCup 2021 - Virtual, Online Duration: 22 Jun 2021 → 28 Jun 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13132 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	24th RoboCup International Symposium, RoboCup 2021
City	Virtual, Online
Period	22/06/21 → 28/06/21

Keywords

2D soccer simulation
Dribble
Feature engineering
Multi-agent matching
RoboCup
Soccer marking

Access to Document

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

Cite this

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. In M. Cakmak, & O. Obst (Eds.), RoboCup 2021: Robot World Cup XXIV (pp. 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. editor / Maya Cakmak ; Oliver Obst. Springer Science and Business Media Deutschland GmbH, 2022. pp. 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",

month = jan,

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doi = "10.1007/978-3-030-98682-7\_28",

language = "English",

isbn = "9783030986810",

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. in 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, pp. 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

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KW - Soccer marking

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

M3 - Conference contribution

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SN - 9783030986810

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

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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. In Cakmak M, Obst O, editors, 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

Abstract

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Keywords

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