IraNad Soccer 2D Simulation Team Description
Paper 2022
Reza Aghayari1 , Shahrooz Shahbazy1 , Mohsen Iranmanesh1 , and Amirhosein
Nikfetrat1
Iranad 2D Soccer Simulation teamr aghayari@yahoo.com
iranad.team@gmail.com

Abstract. In this paper, we will discuss goals, ideas, and algorithms
used in the IraNad Soccer 2D simulation team and introduce our approaches towards improving the algorithms which we utilized and implemented in the team. in this paper, offense, defense algorithms, and theteam strategy like shoot, goalie buddy and will be explained.
Keywords: Soccer 2D simulation · multi-agent decision-making · RoboCup.

1Introduction

IraNad Soccer 2D Simulation Team was established in 2021 due to our teammembers’ hype during the last RoboCup. Although it was online, it carried alot of emotions and hope. The main goal of the IraNad members is to apply theknowledge acquired by reading papers and attending the classes in the development of the 2D soccer Simulation. A lot of TDP reading, game observation, bases
code studies, and soccer strategies were applied to improve accuracy in passing,kicking, man2man marking, decision-making, and other parts of the team. The
IraNad’s team code is based on cyrus14 released base, and all the team members’
efforts were focused on learning and finding ideas by reading released papers,
other teams’ codes, and watching games.

2Related Work

Soccer has been one of the most popular team-based sports globally since around
1860. Soccer is almost the oldest multi-player, real-time, strategic, and partiallyobservable game in which players should cooperate to score more goals thantheir opponent. In addition to cooperating, players should manage different tactics and technical strategies against their different opponents. The world Cup
Robot Soccer Initiative was founded to create a realistic environment similar toauthentic soccer that encourages researchers to employ Robotic intelligence andAI for solving a wide range of problems. The first RoboCup was held in 1997 at
the IJCAI-97 [1], and it offered three competition tracks: real robot league, software robots, and expert robot competition. Among them, the Soccer Simulation2R. Aghayari et al.

2D league (SS2D) as one of the oldest leagues in RoboCup [2] provides a wide
range of research challenges from network-based multi-agent interaction to individual decision making due to it does not have any physical blockage and beinga simulation and inspired a lot of researchers to compute in SS2D each year. We
have studied many papers and TDPs and worked hard in optimizing our code
to improve the capabilities of offensive and defensive actions in total. Althoughmany teams adapt agent2d-base released by the Helios team [3], we decided tocheck newer bases like Gliders2d [4] and Cyrus2D Release team code [5] to findmore incredible ideas and a more leisurely start for our team. as Cyrus team is
the current titleholder [6–9] and Helios Team has won four championships [10–12], we started by reading these papers and moved on to other teams afterward.
We found out a slight improvement over basic behaviors results in a considerable advantage overall from Perspolis[14], Razi[13] and Aras[15]. Osxy Team’s
ideas [16, 17] for offside trap gave us the idea to create a defense line breaker.
FRA-United[18, 19] uses reinforcement learning [20] in brilliant ways in SS2D,
which encouraged us to learn machine learning algorithms.

3Offence

3.1

Shoot

We need to score to win. Moreover, to score more goals, we need more accurate
shots that are more likely to score. To achieve this, we implemented a new shootbehavior consisting of three parts. one part calculates which part of the goal
has a higher risk of missing due to the action and partial noise. The next partreceives the last part calculations and evaluates each point by how likely theopponent goalie or players will intercept the shot. This part also considers theposition count of the ball and opponents, which brings us to the Behind Score
factor. A behind Score factor is a number that uses the goal difference and thegame time. If we scored fewer goals than the opponent, it would help us shootmore by lowering the position count coefficient in the evaluation equation andvice versa. The last part will connect points that are close together, have aclose validation score, find the most extensive continuous connected points, andreturns the center point to the kicker for a shoot. The broader angle will beselected if we have two or more big areas of connected points with identicalvalues.
3.2Mark Breaker

There are a lot of great teams in the 2d soccer simulation league. Almost all ofthem have a very great defense. To create a goal situation, we need to move theball forward and break the opponent’s defense. Therefore developed an anti-markagent behavior whose job is to run around and create distractions behind theopponent’s defense line. As teams have different defense systems, each defense
system’s distraction pattern is unique. It statically increased our goal chanceand granted us better positioning in trade-offing stamina on one or two players.

IraNad Soccer 2D Simulation Team Description Paper 2022

4

Defence

4.1

Goalie Buddy

3

In SS2D, there is much pressure on the goalie; opponents can easily make accurate kick that is more likely to score a goal if our goalie is in a wrong position
which he can not reach and catch the ball. On the other hand, coordination
between goalie and players is critical. In order to relieve the pressure on thegoalie, we defined one of our defensive players as the buddy for the goalie. This
buddy tries to match himself to the state and placement of the goalie. We defined different states and conditions when the ball was near our goal area. States
contain other information like the number of opponents and teammates players
in the area, angle of opponents kicker to the goal, and possible score points foropponents in goal. We categorized these states into three categories, less move,corner shot, and chaos mode. The first category is less move; in this kind ofstate, the goalie and his buddy divide the goal into two areas, find potential
scoring points in both goal areas, and draw lines from the kicker to the goal
points and goal poles to create some covering angles. After that goalie and his
buddy divide these angles into three sub angles. Then the goalie stands perpendicular to opponents in the larger portion of the angle, and the buddy covers thereaming. A visual example of a buddy system is shown in Figure 1. The nextmode occurred when the opponent got to shoot a corner; this mode is similar
to the last one, but this time goalie covers the pole and middle of the goal,and buddy is covering the most dangerous opponent angle (the nearest with abroader angle towards the goal).

Fig. 1. Buddy system - less move on left and semi corner shot on right

In the final mode or the chaos mode, the buddy decides to play as a goalie
because the goalie is out of reach or in a wrong position and can not intervenein the goal until the goalie is back in it; the buddy stands in the larger portion
further from goalie position.

4R. Aghayari et al.

this method helps us achieve a reduction in the average of received goals.
4.2

circle2man markMark is a defensive behavior that some agents tend to stay between opponents
and the kickable opponent, the one with the ball, so that player is unable topass the ball to his other teammates. We developed a semi greedy algorithm
for marking the opponent by drawing angled circles with a radius relative toeach opponent’s danger value according to his movements. This danger value
is calculated from how far a player is from the goal, his angle, and a value
represents how big his kickable area is; an example is shown in the fig 2. AfterFig. 2. Overview of the Marking algorithm.Letter M represent the marker, B theblocker, H the Helper and R the Reserve marker.

sorting opponents by this danger point, the most dangerous opponent must havethe smallest circle. Then, by calculating the distance of players inside that circle
to the most dangerous opponent, the closest player inside the circle to that
opponent would be selected as the marker of that opponent. The next closest
player will be selected as marker-reserve. Suppose there is a circle without any
defender in it. In that case, the algorithm goes back to the nearest circle with amaker-reserve player, switches the marker with marker-reserve, and recalculates
the non-player circle. If there is still no player inside, the algorithm skips thiscircle. In the same way, the algorithm will be executed again and continued
until there is no circle left except the skipped ones. If the skipped circles have

IraNad Soccer 2D Simulation Team Description Paper 2022

5a distance of more than 40 to the goal, the algorithm ends. Else for each circle
within the 40 range of our goal, the none-marker players get sorted by distance tocircles, and the closest player will be selected as a marker for that circle. Remains
players who are not marking any opponent at the moment will be selected as
marker-helpers if the distance of helpers closest marker to its opponent is morethan the half radius of the opponent marking circle.

5ResultsThis paper describes Iranad team approaches such as algorithm implementationand innovation in defense and offense strategy/ In order to show the effectivenessof the strategies series of computational experiments were conducted. The tablebelow shows the summary 1.
WinRate Goal Avg Goal Against
Offense Only 70%
2.72
2.28
Defense Only 76%
1.52
1.08
Total
85%
2.68
0.98
Table 1. The impact of different approaches on Iranad performance against base

6Future WorkWe intend to use neural networks and machine learning to lower the shoot processes, create an accurate multi-agent blocking system, and improve other behaviors such as passing and dribbling.

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