ARAS Team Description Paper 2021
Maryam Teimouri, Armin Sadreddin, Mojtaba Moazen, and Alireza Sadraii
RadK.N. Toosi University of Technology, Tehran, Iranmaryam.teimouri.bd@gmail.com, armin.sadreddin@gmail.com,sadraiiali@gmail.com , mojtaba.moazen.a@gmail.com
http://aras.kntu.ac.ir/

Abstract. This paper is going to represent the output and implemented
methods of ARAS 2D Soccer Simulation Team. These approaches are
categorized in offense strategies and defense strategies. For each part
we have applied different algorithms due to the problem and the chosen
solution. These algorithms are varied in machine learning, matching andplate division algorithms.
Keywords: machine learning, matching algorithms, plate division algorithms.

1Introduction

ARAS Robotics Team has now three subgroups including Soccer Simulationgroup. This part, launched at march 2017 (under another name), has participatedin all the national and international competitions held in Iran since 2017 [1]. We
have been qualified for 7 competitions and won two best presentation awards inIranOpen 2017 and AsiaSpacific 2018, took the third place at ShirazOpen 2018
and the second place at NasirCup 20191 .
In this paper we are going to explain our efforts that have a satisfiable result gained in our three years research. These all are implemented on agent2d3.1.1 [2].

2Related Works

Here we are going to investigate articles published by 2D teams. Helios2019 teamworked on soccer game analysis and proposed a method that adopts an approachof natural language processing and clustering in order to measure the similarity
between teams [3]. FRA-UNIted team implemented machine learning algorithms
to increase communication and goal keeper performance [4]. Cyrus2D team has
researched on optimizing defense decisioning using Qlearning algorithms, offense
decisioning optimization by considering opponent’s players’ affect and improving defensive behavior using communication [5][6][7]. Razi team has a research1nasircup.kn2c.ir/2019

2M. Teimouri et al.

on agent’s action skills and chain action’s state evaluation for agent’s offensive
behavior. They also added marking act to agent’s defensive behavior [8]. Namirateam has developed a match-holding and match-analyzing software (TPAS) andalso implemented a formation detection system [9].
These papers helped us to develop our ideas to modify the source code andevaluating the changes by running games against related binaries.

3Defense Strategy

At first, we are going to define our main idea of the agent behavior in defense
mode.
3.1

Decision making for marking opponents using Hungarian
matching algorithm and Voronoi Diagrams

To increase the chance of making a goal we decided to rise the team ball possession. Making this happen, we tried marking opponent’s attackers while saving abasic position (we call it formation hereafter). So, after searching about marking
algorithms, we implemented what is called zonal marking in soccer: Every player
has a zone to mark within it and this zone is determined by their formation. We
give formation’s nodes as input to our Voronoi Diagrams and every agent marks
the most important opponent in his zone [10]. Although this algorithm seemed
to be an improvement upon base’s simple defense, soon it proved to be inefficient due to the fact that there are lots of situations where there is more than
one opponent in a zone and by using mentioned method, we will never markthem all (see Fig. 1). To overcome this trouble, we used Hungarian matchingalgorithm [11].
In this method we aimed to solve previous algorithm’s problem while minimizing the whole team displacement which has an adverse effect on our agents’stamina. First, we defined three sets of values; mate val which is a value given
to a teammate based on its effectiveness as a defender, opp val which is given
to an agent due to how dangerous it is as an opponent, and an isValid Boolean
variable that’s assigned by Voronoi Diagrams. There are a number of factors
affecting each of these values (e.g. defender’s distance from opponent for former,
opponent’s distance from our goal for latter and many other factors). Next, weused Hungarian matching to find the best set of pairs that contains an opponent and his corresponding marker. This way we could accomplish our 3 mainconcerns: Keeping the team’s formation while saving our stamina and marking
opponents (see Fig. 2).
In order to evaluate this idea, we compared the efficiency of the new code on
agent2d against agent2d itself. We got 36 percent progress in 300 games.

4Offense Strategy

In this section, there is going to be two policies; first policy belongs to the nonkickable players and the second policy manages our kickable player’s behavior.

ARAS Team Description Paper 2021

3Fig. 1. As we see in the picture all the teammates except goalie are marking the mostdangerous opponent in their zone.

Fig. 2. In this picture 5 of opponent’s players (red players) are considered as dangerous
players and are marked and blocked by our team (yellow players) .Dangerous opponents
have a red circle around them.

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4.1

M. Teimouri et al.

Offensive positioning by simulating passes for different positions

In offensive situations non-kickable players need to be in a suitable place toreceive a productive pass and they should be able to get to that point on time.
About the second important consideration, sometimes it is not demanded thatan agent be in a special position to get a pass directly from the teammate
which holds the ball. It can receive the ball through a second pass from anotherteammate. At first, we determine the chain that conducts the ball to each playerof us. Next, we have to find the best place in order to receive the pass. To dothis we simulated each pass our player could receive around its formation andtried to find out whether opponent would intercept each of these passes. Thenwe picked up the best point from the possible choices. We did the same for oursecond receiver considering our first receiver is now the ball holder (see Fig. 3).
As you can see in Fig3 some nodes are generated as scape targets. The values

Fig. 3. Nodes are shown with red circles in the picture. The black values lead us tochoose the best point. The white is the chosen one.

ARAS Team Description Paper 2021

5

are written above them and the while one is chosen.
This process can be done as many times as possible but the more the layers
number are, the less reliable the results become and the more calculations turnout to be. So, we decided to fix layers’ number on three (3 passes).
4.2

Offensive decision using Markov Decision Process (MDP) [12]2D Soccer Simulation environment is stochastic, therefor for each possible act
there are different possible results. The probability of these possible states depends on the environment’s noise. This probability has been calculated by using
the coach in 300 games (only the direction noise). For choosing the best action
Table 1. In this table columns are angles in degree and rows show the ball speed. The
cells’ values are acts’ error in percentage.
duration 0-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 more
0.9-1.2 53.17 16.10 10.24 11.22 4.39 1.95 2.44 0.00 0.00 0.00
1.2-1.5 51.34 19.20 10.71 11.61 2.68 3.12 0.45 0.45 0.00 0.45
1.5-1.8 52.35 21.76 8.82 7.65 1.18 1.18 3.5 31.18 0.00 2.35
1.8-2.1 51.34 20.76 11.61 8.93 2.23 1.79 1.34 0.22 0.22 1.56
2.1-2.4 51.47 16.53 12.53 6.93 4.00 1.07 0.80 1.07 1.07 4.53

we simulate what will happen if the noise takes place. So, the final value for eachact (pass, dribble, or shoot) calculated to apply this method. It helps to takeaction more rational and less risky at the same time (see Fig. 4). As you can see
in Fig4, agent 6 possible passes are drawn with their success probability abovethem.
The difference in the performance between the team with MDP and the agent2d
is about 20 percent in 300 games.

5Conclusion

This team description paper described the approaches and algorithms implemented in the code them. A series of computational experiments were conductedin order to show the effectiveness of the strategies that you can see the summaryin the table below.
Table 2. You can see the tasks’ final result is the table above.
Task
Win Rate Goal For Goal Against Game Count
Defense 85%
2.72
0.96300
Unmark 86%
4.22
2.18300
MDP70%
2.67
1.81300

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M. Teimouri et al.

Fig. 4. Generated passes are in red.

ARAS Team Description Paper 2021

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