Seminar Advanced Information Systems I RoboCup Football Strategies Author: Piotr Jedliński, jedlinski88@gmail.com. Supervisor: Sebastian Müller, sebastian.mueller.4@hu-berlin.de. Abstract: This work contains a brief description of the RoboCup competition with focus on the research in 2D Simulation League. The research consists of analysis of the existent RoboCup football strategies in the direction of establishing the most common and useful strategies amongst the teams, which leads to the main point of the paper: the combination of strategies that is suggested by the author and which could win the competition in Istambul in 2011. Keywords: RoboCup, soccer, robots, soccer strategies, artificial intelligence, football. 1 Introduction The XXI st century is called an era of information and electronics. Some more adventurously foretelling may presume that it will become the age of robots and Artificial Intelligence (AI) as everyday stronger proofs for such staments disembark. One of such proofs has become the RoboCup competition, the arena in which the robots become the main participants and compete in games that every year become a step closer to resembling the human competitions. This paper tries to show that flourishing branch of modern electronics from a friendly and easly accesible perspective. It tries to connect the world of robots (RoboCup Competition) with real-life world (soccer game strategies), showing simultaneously that robots and AI become much more developed that anybody could have expected. 1.1 What is the RoboCup competition? RoboCup is an international robotics competition founded in 1997 and held for the first time in Japanese city Nagoya. The main aim of the competition is the promotion of the research in the field of robotic engineering and artificial intelligence. The participants create and develop autonomous robots that are capable of competing in different activities and categories. RoboCup is divided into 4 different categories:
- RoboCupJunior is a domain targeted at the youngest participants: students from primary and secondary schools. The idea behind this category is the development of the interest in science and robots amongst young generations. - RoboCup@Home - is the category for robots that could be used in everyday life and could contribute to human society. - RoboCup Rescue is the category for robots that are designed to bring help and act independently in dangerous emergency situations where human life is endangered. - RoboCup Soccer is the domain where robots are designed to compete in the game of football. RoboCup Soccer domain is divided into subcategories that depend on the type, size and number of the agents/robots: o Standard Platform League o Small Size League o Middle Size League o Humanoid League (which is additionally divided into 3 categories: KidSize, TeenSize and AdultSize) o Simulation League (which is additionally divided into 4 categories: 2D Soccer Simulation, 3D Soccer Simulation, 3D Development and Mixed Reality Soccer Competition). While all the domains gather each year an increasing number of particpants and bring considerable developments in the field of robotics, the RoboCup Soccer domain remains the most important one. It is the core of the competition with the highest number of participants, the highest investment and the biggest attention. The official goal of the RoboCup competition says: By mid-21st century, a team of fully autonomous humanoid robot soccer players shall win the soccer game, complying with the official rule of the FIFA, against the winner of the most recent World Cup [1]. This statement strongly accents the dominating position of the RoboCup Soccer domain and the research promoting approach in the competition. 2. 2D Simulation League Environment. 2D Simulation League runs on a very sophisticated and advanced virtual environment, which detailed description could full a whole publication. Therefore this paper will be focused only on those aspects of the 2D Simulation League environment that influence the analysis of the football strategies.
2.1 Why 2D Simulation League? As it has already been mentioned, RoboCup Soccer is divided into different categories that differ in many aspects. All of them were assessed in respect to their capacity for the implementation of elaborate football strategies. In the assessment the 3 factors were take into consideration: number of playing agents, their mobility (graded in scale 1-10) and the capacity of AI available for implementing football tactics. All these factors are crucial for the scope of complexity of the strategies. Table 1. Analysis of categories of RoboCup Soccer from the point of view of football strategies implementation scope. Subcategory Number of agents Mobility of agents Standard Platform League Small Size League Middle Size League Humanoid League 2D Simulation League 3D Simulation League 3 agents (standarized robots: Aldebaran s Nao humanoids) Not more that 5 agents (robots size: 150mm or less) Not more than 5 agents (robots size: between 40 and 80cm) 3 agents for KidSize; 2 agents for TeenSize and Adult Size category (robots of the height beween 30 and 160 cm-depending on the category) 11 agents (virtual players simulated in 2D environment) 11 agents ( virtual agents simulated in 3D environment) AI used for tactics 4 quite low Very limited because of the compexity of the process of the movement 6 - medium Moderate capacity 5 - medium Moderate capacity 1 very low Almost non-existent, due to very AI consuming process of movement coordination 10 the same as of human players 10 the same as of human players Very high due to high efficency in movement and environment simulation Quite high due to above average efficency in movement and environment simulation This summary clearly shows that 2D Simulation League is the most convenient category to select as it offers a very friendly environment, which puts almost no limits to the implementation of elaborate football strategies and therefore brings the widest
scope of possibilities for our research. Due to these facts the 2D Simulation League was chosen for the paper considerations. However some important limitations cannot be ommited. The players AI, even though most developed in whole RoboCup competition, is still quite limited, which makes the agents behaviour complexity still far behind the real-life. Moreover, eve though the mobility of the virtual agents is the same of human players, they are represented by dots, which considerably limits the possible foot-tricks and dribbling. Another issue derived from a very basic graphic representation is the scope of skills of the goalkeepers: they do not have hands which makes their catching ability somehow even more abstract. These are just some of the limitations of the 2D Simulation League compared to the real-life, however necessary to mention from the point of view of this paper. 2.2 Capacity of the players. The simulated agents have just four actions for physically manipulating the world: turn, dash, kick and catch (only for goalkeepers); and can perform only one at the time during the game. The actions are executed with different precision and velocity, which depend on the parameters of the agent. The agents, even though are virtual, are binded by stamina constraints in order to provide real-life players limitations. The stamina assigned to each player is divided into two categories of components: replenishable and non-replenishable. There are several replenishable components, which decrease with excessive running or when kicking the ball, however, after some time when the player is not using his stamina they can increase to the previous level. On the other side there is only one nonreplenishable component: recovery (whose value states the rate at which the player recovers his replenishable stamina components), which decreases only when the level of stamina of the player drops below the threshhold, however it cannot be incremented during the game, therefore is crucial for the player. The players are also binded with human scope of perception. Their vision is constrained: it captures less than 180 of the landscape and becomes less precise with the increase of distance between the agent and the observed object. The hearing is limited with similar logic applied. The player can only hear and communicate with the nearest agents and there is the variable of noise applied, which when of considerable value, may prevent the player from communicating at the given time of the game. Beside the players there is also a virtual coach participating in the game. Even though he does not take active part in the game, efficent usage of this agents capacity can determine the final outcome of the game. This is caused by the basic function of the virtual coach that is the abillity to assess the situation on the field and adjust the tactics of the team. When agents on the field are mostly occupied by playing they do not have much AI capacity and perception clearness left for the tactic decision
making. The coach on the other hand is occupied only with the analysis and has less disturbed perception which enables him to make more correct decisions and adjustments. This ability is even more important, when the rule of non-assistance of the humans during the game is recalled. 2.3 The size of the field and duration of the game. The football server is a two-dimensional simulation therefore there is no notion of height for any object on the field. There are up to 22 players on the field at a time along with 1 ball, all of which are modeled as circles and there are also several visible markers, including flags and side lines, distributed around the field, like in real-life football pitch. The location of the players is determined by the servers parameters which can be determined by 10 198 states: each of the 22 agents can be in any of 680 x 1050 x 3600 positions. When taking into the account the ball, velocities and past states, the actual state space is much larger than these values, which shows how well developed is the simulated environment and how many possibilities it offers. Each game is composed of two halves 5 minutes each. The time in the football server is divided into cycles, with each cycle of duration of 100 ms, which gives us 6000 simulation cycles per game. This notion is very important as the agents make independent decisions and perform them in a predetermined number of cycles, which serve as a measure for the agents behaviour efficency and speed. 3. Overview of the most common and basic notions in successful football strategies in 2D Simulation League. This part of the paper contains a summary of a research done in order to determine the reccurent schemes and notions existent in strategies of the most of RoboCup 2D Simulation League teams. For this purpose the author analyzed two very detailed publications of Peter Stone [2] and the group leaded by T. Takahashi [3] as well as Team Description Papers of all the teams that took part in Final competition in Singapore in 2010. Team Description Papers proved to be very useful and informative documents as they convey the main focus area of the given team, future plans for research and stratey developments. The analysis showed that almost all the teams (more than 90%) use the 1-4-4-2 formation consisting of: one goalkeeper, 4 defenders, 4 midfielders and 2 attackers. This is caused by the most efficent distribution of roles that agents perform during the game and the possibility to adapt to the opponent. The teams that tried different formations often encountered distortions in adjusting their tactics to the situation on the field, therefore it is uncommon to deviate from this scheme. As it is shown in next sections of the paper the players assigned to the same line of formation often play different roles in the team depending on whether the team is attacking or defending.
What proved to be very common amongst the teams, especially the best ones, is the ability to learn the behaviour of the opponent, which very often is the key to success. The RoboCup teams use a variety of tools for learning that differ in complexity, accuracy and purpose. Some of the teams focus on learning the opponents attacking schemes in order to adjust its defensive tactics to become more efficent in intercepting the ball. Other teams focus on the learning of the opponents defence schemes in order to perform more accurate attacks. There are also some other groups, though in minority, that use their learning systems for variety of other reasons, as for example: retaining the ball possession for the most of the game or clarifying the roles of the opponent players in order to mark them more efficently. In the section 4 there is a presentation of the most useful tools that got incorporated into the winning strategy. From the aforementioned characteristic emerges other very important notion: it is essential to have a flexible teamwork structure. Those teams that are putting rigidities on the movement and the decision making of their players usually fail to perform the learning and adjusting process properly. It is common that these teams are quite efficent in defending however they loose in the aspect of attacking and scoring goals. Therefore it is a very common notion to assign to each agent more than one role and leave the coach broader space for decisions for tactics adjustments. The last but not least pattern that emerged in most of the RoboCup teams is the well-developed system of communication between the agents. Even though as mentioned: the players are restricted to human scope of perception (which also concernes exchanging information) it is still a very useful tool that is essential for the sound coordination of the team. Therefore it becomes compulsory for an effective performace of all the other notions mentioned in this section. The agents have at their disposal the say command which serves as the means of transfering information to the teammates nearby. This tool proved to be especially important in constructing offensive actions and retaining the possesion of the ball in dangerous areas of the field. 4. Research on distinctive strategies of the succesful RoboCup teams and formulation of the winning strategy. This section is a meritum of the paper. It presents the research done in order to determine the distinctive strategies amongst the best teams in the 2D Simulation League that could be combined, implemented and developed into the winning strategy. For the process the author used the Team Descritpion Papers of the best 4 teams of the Singapore 2010 competition, which were (in order from the 1 st to 4 th final standing): - HELIOS2010 (from Japan)[4], - Wright Eagle (from China)[5], - Oxsy (from Romania)[6],
- ESKILAS (from Iran)[7]. These publications were analyzed from the point of view of the comliance to the basic notions from the previous section. The author made the basic assumption, that the strategy that could win the Competition in Istambul in 2011 should be based on these most common notions, as they have proved to be most successful as well as efficent and that the research in this section should seek for the further developments of these ideas within the strategies of 4 best teams. Indeed it showed to be a correct assumption, as all aforementioned teams focus mainly on developement in common strategies by implementing their own distinctive ideas and tools. Therefore this section clarifies the combination of the strategies of the author for the winning strategy. The selection is made based on the principle of obtaining the coherent and efficent strategy, that will combine all the best aspects of the 4 best teams with compliance to the findings of the previous section. When determining the attitude of the team, the author decided to choose an attack-minded, flexible and teamwork playing team like Arsenal London or FC Barcelona. Beside the personal reasons, these teams were also chosen for their reputation of the best and most efficent teams of the real-life world. For the clear layout of these section the winning strategy ideas have been grouped into 2 categories depending on the phase of the game they address, one that concerns goalkeeper and one joint category for the whole team. 4.1 Winning strategy : notions that regard the whole team. This part of the winning strategy should be regarded as the essential one. This is for two reasons: the assumption that the team should have a flexible and teamwork oriented structure and the findings in the common strategies section that highlight the need for good coordination. Therefore the wining strategy inherits two very important and efficent tools from HELIOS2010 and ESKILAS teams: a learning mechanism - unsupervised learning method based on Constrained Delaunay Triangulation(CDT) and teamwork efficency upgrade - Refreshing ball owner s vision mechanism respectively. 4.1.1 Unsuprevised learning method based on Constrained Delaunay Triangulation. This tool is used as a learning mechanism that wants to determine and analyze the unknown opponents formations. It is a very important notion as it helps the team to undertsand the filosophy of the opposition and adjust to it. As it is visible in the next subsections of this paper, very often other ideas are based on this concept and therefore are strongly dependent from it. This mechanism is very complicated and developed, therefore it is impossible to explain it in detail here. However the idea can be shortly introduced in a very simple and composed way. This tool divides the football field into a set of triangles, which become an input plane region. The first set is made randomly and is standarized for all games.
Figure 1. Learning method based on CDT example of the initial division of the field made by HELIOS2010 team. During the game the behaviour of the opponent, i.e. his movement and directions in which he plays the ball, is translated into set o data treated as an input into our plane regions triangles. Because of that data triangles are restated and redrawn. In order to do that there is another tool used here as a backup to CDT it is the Growing Neural Gas (GNG). It is an unsupervised learning method that uses vector cuantization to generate a set of CDT triangles in an efficent and fast way. HELIOS2010 team members claim that these methods can be applied in the future not only in RoboCup, but also for online coach s game analysis, especially opponent team formation modeling, in realtime.
Figure 2. Example of redrawn triangles with use of CDT with GNG engaged made by HELIOS2010 team. As it is visible from the picture, the set of triangles has changed considerably. In this example it is observable that the opponent team usually moves to their left side and exchange more passes there. Therefore it could prove useful to adapt the tactics and strenghten the right side of defence, to stop these incerasing attacks and start trying to counterattack by the left side, which could exploit the weakness of the opponents right flank. 4.1.2. Refreshing ball owner s vision mechanism. This tool is derived from the strategy of ESKILAS team and would prove to be a very effective notion that would considerably enhance the communication in the team and the coordination of the tactic. The tool is based on the basic command: say. In this paper the example of retaining the ball possession in own part of the pitch is used for presentation of the idea. For instance: when the teammates see that the player with a ball cannot move forward because of the opponents blocking the way and that there are other opponents at his back that he is unaware of they can pass him the information about the positioning of all opponents near him. This tool was chosen because of its speed and accuracy: it takes only 1 cycle to pass the information about 4 opponents with accuracy of 0,1 meter.
Figure 3. Refreshing ball owner vision mechanism- example situation. In this example situation the ball owner is surrounded by four opponent players two of them that he has noticed and two that he is unaware of. In such situation, one of his fellow players, any that is noted with a number, can in just one cycle pass the information about the positioning of all the opponent players, which will prevent the ball onwer of loosing it in the dangerous part of the pitch. This is only one of the possible contribiutions of this mechanism. It can be also successfully used in passing, changing positions and correcting the tactics by the coach. 4.2. Winning strategy : notions that regard the goalkeeper. In a well organized and attack-minded team the goalkeeper usually is passive for the most of the game. However he sometimes is called into action and usually his main duty is to determine correctly when is the perfect moment to leave the goal and rush in order to intercept the ball or shorten the angle for the striking attacker. Therefore the winning strategy contains one useful tool that helps the selection of the goalkeeper that will determine most correctly his decisions in the goal. This tool is called Heteregonous Goalie Selection Mechanism and is derived from the strategy of the Wright Eagle team. In this mechanism a virtual goalie is placed at point (0;0) with randomized body direction and velocity and is ordered to run to a nearby opponent when he is havng the ball in order to intercept it quickly. The tools assumes that the number of cycles of running and the catchable probability of the last cycle is recorded as cycle and prob. Subsequently the evaluation value of such heterogeneous type during this simulation process is being calculated by the simple formula as follows. eva =prob/ (cycle + 1.0) (0) Then a heterogeneous type is determined by calculation of the sum of eva of many different simulation processes. It is obvious that the larger the sums are, the better the real ability of catching the ball of that heterogeneous goalie type is. This mechanism helps to determine the best goalkeeper for the game and helps to determine his ability to disrupt the opponents offensive actions when needed.
4.3 Winning strategy : notions that regard the defending phase. This aspect of the winning strategy was derived and combined from the ideas of the ESKILAS team strategy and from Peter Stone s publication. For the better organisation of the defending mechanism the players that take part in the process have been divided into two groups: guardians and markers. This categorization is due to very different roles that both groups perform in the phase of defending. 4.3.1. The guardians. The guardians are the two midfielders that beside the playmaker and the offensive player roles are also assigned in the winning strategy to the defending role in the situation when the opponent is attacking. In modern football this kind of a midfielder is named box-to-box midfielder. The role of the guardian is to block the opponent in his offensive action already in the middle part of the field up to the defence line. The blocking in this paper means approaching the opponent and trying to intercept the ball directly or press the opponent away from the penalty area and force him to make incorrect pass or other error. The process of assigning the best suited midfielders to perform the block in a given situation is quite simple. All the players in the team voice their two best candidates for the role by the say command. During the defense phase the players hear all the time this decisions of their teammates and therefore know whether they should block in the given situation or not. If a player is called by enough teammates enough number of times, he knows this is the time for his reaction. In some situations it may be suitable to call only one guardian to action. It all depends on the positioning of the opponents and fellow players (as it is visible: the unsupervised learning method based on Constrained Delaunay Triangulation(CDT) and Refreshing ball owner s vision mechanism play an essential role in this mechanism). When the midfield player already knows that he should block the opponent he has first to decide how to approach him. An important step is finding the route of opponent s dribble, better to say, guesstimating opponent s dribble target. In the winning strategy the most contributing factors are: opponent s position, body direction, velocity, recent moves and also the blocker s post in our current formation. After determining the most likely route for opponent s dribble, the next step is to find the best interception point on the opponent s dribble route through an algorithm similar to ball-intercept. After a successful approach to the opponent we have two types of algorithms for obtaining the ball possession (if two guardinas have been assigned: one performs the first method and the other the second one): - Side-dash-enabled Close Block (safe block): This option is performed in the dangerous areas of the pitch, i.e. near the teams penalty area, as it is a safer option, which helps to avoid unwanted fouls and free kicks in a position where the opponent could try a direct strike at the goal. When the guardian uses this facility for obtaining the ball, he precisely
moves in four directions to exactly stand on the opponent s dribble route. In this way, the opponent can no longer easily move over the blocker as this will cause strict collision between two bodies. - Direct Close Block In low risk places, i.e. away from the penalty area, where the players can try more aggressive actions, the guardian directly approaches the opponent and in frequent cases he practically pushes the opponent and finally the ball is kicked or tackled away. 4.3.2. Markers. Markers are all 4 defenders which have an assigned role of marking the opponents players and trying to intercept the ball whenever possible. In this notion a more advanced tool of assigning most suitable players for the role has been used, as the markers operate closer to the goal and more accuracy is needed. The tool used in this situation is Mark Table Generating Mechanism. As it needs considerable focus on the part of the agent and a complete vision of the field, in the winning strategy the virtual coach is made responsible for using this tool. In the beginning of the game, this coach observes opponents behaviors in order to determine the agents who should be seen as main and most dangerous attackers. These players then are gradually determined and categorized using a statistical approach. For this purpose a mark table is generated in which each opponent attacking player is assigned to a defender that is best positioned and considered to be the best one to handle the pressure. It is achieved by using a bipartite weighted graph matching algorithm. In that model, every player is a node of the graph, opponent players are in one part and teammate defenders are in the other part. Between any opponent node and any teammate node there is an edge with a weight that is proportional to their distance in the field. The virtual coach periodically transports the last updated mark table to all defenders via say command. As the Mark Table Generating Mechanism is centrally created, there is the highest level of coordination achieved and possible conflicts are avoided. After matching the defenders to the opponents attackers the agents have to decide how to approach and try to intercept the ball. In this part, yet again, the notion of division between safe and dangerous situations is made: - physical marking in dangerous situations: in dangerous parts of the pitch, like a penalty area, the defenders try to stick to the opponent and move in short distance between the target and the ball in order to cut the passes, - physical marking in normal situations: in such situations the defender stays in a moderate distance behind the attacker and observes his movement. The defender tries to intercept the pass only in situations where he sees that there is a high chance of success as not to break the defence line meaninglessly.
4. Winning strategy : notions that regard the attacking phase. In this section the attacking aspects of the team are covered. As it was stated n the basic assumptions, the team using the winning strategy is offensively minded and encouraged to play as a team, not selfish individuals. The paper has already introduced tools neded for a good coordination, positioning and cooperation on the pitch, therefore the aspect of the teamwork is already deeply covered. Nevertheless the aspect of efficent attacking as the last notion to be discussed is not the least important, otherwise, as an icing on the cake is the factor that gives the winning strategy the most important factor for winning, goals. This section is divided into two subsections: behaviour of the agent when he is in possesion of the ball and behaviour of the agent when he is without the ball. In this part the ideas of the Oxsy team are being used. 4.1. Attacking phase: the agent in the possesion of the ball. When the player has the ball, essentially he can choose how to behave by choosing one of the 4 options: - Refraining from losing the ball (avoid risky moves in the defensive area). - Taking/dribbling the ball forward towards the opponents goal. - Getting the ball to a teammate who is free from his opponent s marking. - Shooting effectively. The team that uses the winning strategy is playing teamwork football, therefore the option of dribbling should be regarded as very rare and the agents are programmed only to use this idea in the absence of teammates on the opponents half or during time-wasting at the end of the game. In this phase of the game it is difficult to introduce any very efficent and accurate tool as the number of variables (other agents movement, the scope of reaction of the ball, power with which to hit the ball, stamina of the agent etc.) is so high, that considerable discrepancies could occur. Therefore the main idea in the winning strategy is to program agents to avoid risky passes in the middle section of the field, especially backpasses, and in the final third of the pitch to suddenly accelerate the action in order to mislead the opponent by the change of the tempo and introduction of the bold solutions. Moreover the agents, by the excessive use of the Refreshing Ball Owners Mechanism are encouraged to always pass the ball to the teammate that communicates about his favourable positioning and the opportunity for a killer pass. It means that the 3 option: Getting the ball to a teammate who is free from his opponent s marking is dominant in the winning strategy. The matter of shooting in the winning strategy is yet again influenced by its teamwork character. The agents should be programmed in such a way, that the long shots, like dribbling, should be considered as a last resort: only in situation of the absence of the teammate or other efficent solution. Therefore shooting in the winning strategy is performed from shorter distances, after creating a good position for a striker, from which there is a high probability of success.
4.2. Attacking phase: the agent without the ball. Due to the character of the winning strategy this part of the attacking phase is the most important one, as the behaviour of the player without the ball and his positioning is the determining factor of the development of the offensive action. The player without the ball, in the winning strategy can perform one of the three functions: - Preventive covering: the offensive players that stay behind, that is: between the opponents defence and attack line, try to position themselves in such place that in case of loosing the ball to the opponent, they become an obstacle that slows down or stops the attack. All the offensive players, who remain positioned behind the line of the ball, are considered to be in preventive covering. - Support: an offensive agent that is available to receive the back pass or the horizontal pass is a supporting player. As it was already mentioned, the winning srategy discourages from long back passing, therefore the players will try to position themselves in the vicinity of the horizontal line of the ball. In such situation the agent s support is referred as encompassing because he can receive the ball either some steps forward or some steps backward, depending on the game situation. - Asisst: an offensive agent that makess himself available during an attack in front of the ball owner is being reffered as an assisting player. He can receive the ball in two ways: by getting unmarked or wth a opponent defender at his back. He has the opponent at his back when he comes towards the ball or when he cuts towards the corner flag. He tries to receive the ball unmarked when he cuts towards the opposing goal or when he penetrates to receive the pass from his teammate. The preventive covering and support functions have already been covered in previous sections by introducing the cocnept of guardians and notions for the dynamic positioning and supporting teammates. The las, but not least concept to be discussed is the introduction of the tool for efficent behaviour of the two attackers in the winning strategy, who for the most of the game have one role in the team: acting as an assisting player. For this reason the attackers are programmed to behave in respect to three patterns: - Both forwards go towards the teammate with the ball:
Figure 4. Example of two patterns of attacking player moving towards the ball to receive the pass. In the first situation the attacker 11 moves toward the pass of the midfielder 10. When he gets the ball he immeditely plays one-two with player 7, who, after running towards the ball in the moment of receiving the ball by the player 11, changes direction and runs into the penalty area where he receives the one-two pass from player 11 and at the same time misleads the opponent defenders. In the second situation both attackers run into free spots in the penalty area while simultaneously changing their positions, which creates trouble for the opposition defenders. The midfielder seeing this movement passes the ball into one of the two free spots in the opposition penalty area, which is received by either player 11 or 7. - Both forwards move forward in depth: Figure 5. Example of two patterns in which two attacking players move into depth of the opponents penalty area. In the situation 3 both attackers move in deep which makes them difficult to cover simultaneously for the defenders. The midfielder in such situation can try a high pass to each of them, which can be followed by a header from a short distance. The situation 4 slightly differs from the previous one. This time attacker 11 is moving into deep free space of the opponents penalty area while the other is running into a space previously occupied by the attacker 11. By this means the attacker 8 can run into a space created by the movement of the attacker 11 and receive a low pass by the midfielder. The attacker 11 meanwhile is running into deep where he is expecting, like in the previous pattern, a high pass for a header. - The forward goes towards the ball while the midfielder goes forward in depth:
Figure 6. Example of two patterns where one attacker is running towards the midfielder while the other one is running into the penalty area. In both examples the pattern is very similar. One of the attackers runs towards the midfielder in order to mislead the opponent defenders and break their defence line, while the other attacker runs nto a free space deep into the opposition penalty area where he can receive a pass in a very favourable position. 5. Short summary of the winning strategy and the issue of verification and validation. The winning strategy introduced in this paper is assumed to gather all the best notions of the best teams in the RoboCup 2D Simulation League. The author tried to combine the teamwork flexibility and tactical adaptability of the HELIOS2010 team, defensive rigor and well-coordinated organization of the ESKILAS team, attacking threat of the Oxsy team and other very useful ideas of other succesfull RoboCup teams. Altogether the team that uses winning strategy is meant to be a wellorganized team that keeps the possesion of the ball for most of the match, uses all the virtues of the team-play and is capable of altering the tempo of the game swiftly and suprisingly for the opponent team. All these ideas have been summarized in the table. Table 2. Summary of the presented strategies. Common Strategy Distinctive Strategy/Tool Team from which it is derived Part of the team it concerns Learning the Unsupervised HELIOS2010 The whole team opponents behaviour Learning Method with CDT Efficent communication Refreshing Ball Owner s Vision Mechanism ESKILAS The whole team Efficent defending- Heteregonous Wright Eagle Goalkeeper
adjusted to the opponent Efficent defendingadjusted to the opponent/ efficent use of the virtual coach Efficent attacking teamwork/flexible play Efficent attacking teamwork/flexible play Goalie Selection Mechanism Guardians/Markers ESKILAS Defence/Midfielders Agent with the ball Oxsy Midfielders/Attackers Agent without the ball Oxsy Attackers For the time being the winning strategy exists only on paper and it has not been adopted for any RoboCup teams. Therefore it is not certain whether the implementation of this combination of strategies would be feasible. During the introduction of the winning strategy it could occur that it combines too many complicated tools that together surpass the capacity of the RoboCup football server. Also it has not been verified, therefore it is uncertain whether this strategy would be truly winning one and would make the team triumphant in the RoboCup 2011 in Istambul. Even though on paper it seems well elaborated and complete, in reality some schemes could prove to be self excluding and cause unwanted and not coordinated behaviour of the agents on the field. Both of the aforementioned issues should be regarded as a future work as it would be a natural development of the project. The coclusions derived from the processes of verification and validation would probably lead to another, more detailed, research combined with simultaneous imporvement of the winning strategy. Nevertheless, the paper shows a considerable scope of the literature and the strategy possibilities that describe the environment of the RoboCup Competition. The paper can be perceived as a useful benchmark for any new teams in RoboCup or students interested in increasing their knowledge in this robotic competition. This paper should be read along with two other papers that are closely related to it: the publication of P. Stone and the work under leadership of T. Takahashi. All three papers would give a very broad perspective on the topic of the RoboCup and soccer strategies. Additionally, the work combines some useful information about the mechanisms that rule the strategies of the most succesfull teams and tries to create one joint strategy that could be used in RoboCup. This strategy, even though not tried in real life, can be used as a starting point in defining the teams RoboCup strategy and implementing different tools and mechanisms. The RoboCup competition is surely a very interesting event. It encourages research and development of the robotics not only amongst scientists, but as this work shows, also in business related academic environment. Robocup has established itself
some very difficult goals, but as this paper tried to conclude, it is surely developing in a very interesting and promising direction. References 1. RoboCup official website, www.robocup.org/. 2. Stone, P.: Layered Learning in Multi-Agent Systems. PhD Thesis, Carnegie Mellon University, Pittsburgh (1998) 3. Takahashi, T., Lakemeyer, G., Sklar, E., Sorrenti, D.G.,. : RoboCup 2006: Robot Soccer World Cup X (2007) 4. Akiyama, H.: HELIOS2010 Team Description Paper, (2010) 5. Aijun, B.: WrightEagle Team Description Paper, (2010) 6. Marian, S.:Oxsy Team Description Paper, (2010) 7. Zanjani, M. A.: ESKILAS Team Description Paper, (2010) 8. Norouzitallab, M.: Nemesis Team Description Paper, (2010) 9. Reis, L.P.: FC Portugal Team Description Paper, (2010) 10. Ming, G.: Hfut Engine 2010 Team Descritpion Paper, (2010) 11. Borujerdi, F.H.: FC Pars Team Description Paper, (2010) 12. Uenishi, T.: opuci 2D Team Description Paper, (2010) 13. Gspandl S.: KickOffTUG Team Description Paper, (2010) 14. Dorer, K.: Modeling human decision making using extended behavior networks. In: RoboCup, pages 81 91, (2009) 15. RoboCup Singapore 2010 Tournament official website, http://www.robocup2010.org/ 16. Stone, P.: A Winning Approach to Robotic Soccer. MIT Press, (2000)