USING A DATAWAREHOUSE TO EXTRACT KNOWLEDGE FROM

ROBOCUP TEAMS

Isabel Gonzalez, Pedro Abreu and Luis Paulo Reis

FEUP - Faculdade de Engenharia da Universidade do Porto

Rua Dr. Roberto Frias, 4200-465 Porto, Portugal

DEI - Departamento de Engenharia Informatica, Rua Dr. Roberto Frias

4200-465 Porto, Portugal

LIACC - Laborat

orio de Intelig

encia Artiﬁcial e Ci

encia de Computadores

Rua Dr. Roberto Frias, 4200-465 Porto, Portugal

Keywords:

Knowledge Extraction, DataWarehouse, Game Analysis, Robotic Soccer, RoboCup, Simulation.

Abstract:

RoboCup is a scientiﬁc and educational, international project that involves artiﬁcial intelligence, robotics

and sport sciences. In these competitions, teams of all around the world participated in distinct leagues. In

the begining of the Coach competition, one of RocoCup leagues,the goal of the researchers was to develop

an agent (Coach that provides advices to teammates about how to act and with improve team performance.

Using the resulting improved coach agent, with enhanced statistic calculation abilities, a huge amount of

statistical data was gathered from the games held at Bremen 2006. This data was then stored and treated

in a data warehouse system obtaining a good high level perspective/knowledge of the RoboCup simulated

soccer tournament. According to the results, the team that represented our country, has a much more goal

opportunities in comparison with the majority of the teams, but this team did not score many goals. In terms

of more occupied regions, the best four teams in the tournament did not occupy many times the left and right

wings, compared to others regions. In the future the our country team needs to develop new strategies that use

these two areas preferrentialy in order to achieve better results.

1 INTRODUCTION

RoboCup is a scientiﬁc and educational, international

project that involves artiﬁcial intelligence, robotics

and sport sciences. The main goal of RoboCup is to

develop a team of fully autonomous humanoid robots

that can defeat the human world champion team in

soccer by 2050.

Since the ﬁrst ofﬁcial RoboCup games in 1997,

in which over 40 teams participated (real and sim-

ulation combined), there have been lots of other in-

ternational and regional competitions and scientiﬁc

conferences, related with it. In these competitions,

teams of all around the world participated in several

distinct leagues and researchers presented scientiﬁc

work about their approaches to several sub problems.

Nowadays there are many different leagues

divided into four main classes: RoboCup Soc-

cer, RoboCup Rescue, RoboCup@Home and

RoboCupJunior. In these classes, there are also

several sub leagues. For example in RoboCup

Soccer competitions there are many sub leagues like

Soccer Simulation leagues (2D, 3D and Coach) and

robotic leagues (small-size, middle-size legged and

humanoid). One of the competitions present in the

Soccer simulation leagues is the Coach competition

in which research groups have to develop a Coach

Agent capable of high-level action and intelligent

analysis of the simulated soccer game. In the begin-

ning of this competition, the goal of the researchers

in this league, was to develop an agent (Coach) that

provides advices to teammates about how to act and

with this improve team performance. The commu-

nication system between the coach agent and his

players was supported by a standard coach language

called CLang (Federation, 2007). This language

was developed based on COACH UNILANG (Reis

and Lau, 2002) language created by FC Portugal

in 2001 which enables high-level communication

between a coach agent (or human coach) and a soccer

robotics playing team. Clang started to be a very

simple language but evolved into a very complete

511

Gonzalez I., Abreu P. and Paulo Reis L. (2008).

USING A DATAWAREHOUSE TO EXTRACT KNOWLEDGE FROM ROBOCUP TEAMS.

In Proceedings of the Tenth International Conference on Enterprise Information Systems - DISI, pages 511-514

DOI: 10.5220/0001715405110514

 SciTePress

language for coaching soccer teams, containing most

of the features introduced originally in COACH

UNILANG.

The main goal of the coach competition was

changed after its ﬁrst four editions (2001-2004) trans-

forming the competition into a game analysis chal-

lenge (Kuhlmann et al., 2005). The league has been

used, since then, to extract usefull information about

the game. In the context of this league, in the past

years, many advances have been made in these re-

search topics: Development of team opponent mod-

els (players individual models and teams collective

models (Kuhlmann et al., 2006); Methodologies for

using Coaching to help teams to improve their per-

formance in the simulated robotic soccer domain (Ri-

ley et al., 2002); Adjustable autonomy in real-world

multi-agent environments (Scerri et al., 2001).

The main goal of this research is to extract the

highest knowledge about the way of playing of the

teams that participated in the 2D simulation league of

RoboCup World Championship 2006 held in Bremen,

Germany. Using the available coach program of FC

Portugal team (team, 2007) as a base, we have imple-

mented new algorithms for calculating several statis-

tics. Using the resulting improved coach agent, with

enhanced statistic calculation abilities, a huge amount

of statistical data was gathered from the games held at

Bremen 2006. This data was then stored and treated in

a data warehouse system obtaining a very good high-

level perspective/knowledge of the RoboCup simu-

lated soccer tournament.

2 PROJECT ARCHITECTURE

In order to achieve the best knowledge of the playing

style of all robotic soccer teams participating in the

last simulation league tournament of RoboCup, this

project used the architecture illustrated in ﬁgure 1.

In RoboCup 2006, as in others similar tourna-

ments, many teams compete together to achieve the

best result having as main goal, obviously, to win the

competition. In order to start the simulation the play-

ers (agents) need to connect to a server (soccerserver)

and after that the simulation could be started. At

the ﬁnal of the game, the soccer server generates the

corresponding log ﬁle, which characterized the entire

game, including the players and ball positions and

velocities and other relevant information. The ﬁrst

phase (1) of this project consisted in collecting all log

ﬁles from RoboCup 2006 tournament. In the next two

phases (2 and 3), the coach agent (introduced in the

previous section), using the log ﬁles then extracts the

maximum information about the game and calculates

different types of statistics e.g. number of successful

passes, number of goals etc. After that, in phase 4, the

Data Warehouse (DW) was created and the statistics,

that were previously calculated, were transformed in

order to be easier to store in the DW. The statistics

were then stored in an appropriate format in the Data

Warehouse. In the ﬁnal project phase (5), after the

storage of all tournament data in the DW, using SQL

queries, we aimed at extracting all kinds of useful

high-level information about all robotic teams indi-

vidual and collective playing style.

Figure 1: Project’s Architecture.

3 GAME STATISTICS

According to the Coach Competition rules, the coach

has to analyze log ﬁles of a given team and discover

its playing pattern. The main challenge is to use the

low-level information contained in the log ﬁles (po-

sitions and velocities) to calculate and appropriately

use high-level information of the match. The games

statistics calculated included: passes (and its results),

ball losses and recoveries, game situation occurrences

(corners, free-kicks, among others), goals and scoring

opportunities.

3.1 Field Regions

With the purpose of extracting information about the

places where there is a higher occupation rate for both

teams in a game, we have separated the ﬁeld in eight

regions: our middle, their middle, our penalty box,

their penalty box, our left wing, their left wing, right

wing and their right wing.

ICEIS 2008 - International Conference on Enterprise Information Systems

512

The region delimiters are the following 2 for left team,

and the opposite of right team.

Figure 2: Considered Field Regions.

These regions are built giving the points of a rectan-

gle that deﬁnes each region in the ﬁeld. The results

depend on the team analyzed, the side that team starts

to play and if you are in the ﬁrst half or the second

half of the game. All these statistics are saved in a

ﬁnal game ﬁle that will be then analyzed.

3.2 Pass Information

A pass may be considered when a player kicks the

ball with enough force in a speciﬁc direction, aiming

to a teammate that is supposed to receive the ball.

Regarding passes, the statistics calculated are the

total of passes, the percentage of passes in each part of

the game, the percentage of passes of each team, the

same with each player (passes executed and received),

the percentage of successful passes in each part of the

game of each team. Pass statistical information is also

calculated by each ﬁeld region.

3.3 Ball Recoveries and Losses

A ball recovery occurs when a player from the team

has the ball possession in a given cycle (e.g. may kick

the ball) and he losses the ball possession for a mem-

ber of the opponent team (a player from the opponent

team may kick the ball). This statistic is the opposite

of a ball recovery. When a team is able to do a ball

recovery, the other team looses the ball possession.

Regarding this statistic, the system calculates the

total of ball recoveries and losses, the percentage of

recoveries and losses in each part of the game, the

percentage of recoveries and losses of each team, the

same with each part of the game. Statistics are also

calculated by each ﬁeld region.

3.4 Game Occurrences

This game parameter summarizes the occurrences

that happened in the game. These occurrences can

be free kicks (right or left), goals (right or left), off-

sides, corners, etc. In this situation we calculate the

total occurrences, the percentages of each occurrence

and the percentage of occurrences in each region.

3.5 Goal Scoring and Opportunities

A goal scoring opportunity occurs when an attacking

player has the ball possession in a dangerous area.

Dangerous area is a subjective concept that we esti-

mate as a region sufﬁciently near the opponents goal

that a shoot may be successful. In this statistic cal-

culation we estimated the total of goal opportunities,

the percentage of opportunities in each part of the

game, the percentage of opportunities of each team

and player and the same with the halfs of the game.

Besides this, it is also calculated the percentage that

each region is visited when there is an opportunity.

4 DATA WAREHOUSE

In order to get information about statistics, the ﬁnal

game ﬁle was split into several different ﬁles, one for

each type of statistic. In each ﬁle the data was sep-

arated by tabs considering the future storage a data

warehouse. Although it could be a good choice, XML

is not being considered as a storage format because it

implies deﬁning a schema and then validating each

ﬁle with this schema and, in the process of storage,

its use would decrease the performance of all process

comparing with the tabs separation method.

The reason for using a data warehouse instead of

a conventional database is mainly because, in this

project, the goal was to collect a higher number of

ﬁles at once and after that do some interrogations

in the data warehouse. The performance of the data

warehouse in terms of time delay is also critical so

the data warehouse seems the best option in this case.

In order to represent most of the interesting soccer re-

lated statistics the following data warehouse was been

deﬁned.

In this structure three distinct dimensions were de-

ﬁned: Participation, Game Summary and Classiﬁca-

tion. The Participation is the dimension that had all

information about the participants of the game like

referee, trainer and player, ﬁltered by data and league.

The Game Summary dimension is referred to all game

statistics calculated per team like the number of at-

tacks, number of assists, ball recovery among others.

Finally, the Classiﬁcation dimension has information

about the ﬁnal results that teams achieve in a spe-

ciﬁc competition. In this project only the game sum-

mary dimension have been used mainly to simplify

USING A DATAWAREHOUSE TO EXTRACT KNOWLEDGE FROM ROBOCUP TEAMS

513

the analysis that would be confusing using all kinds

of data for all dimensions, for all the teams.

5 RESULTS AND DISCUSSION

For a better understanding this section is divided in

three parts.

5.1 Goal Opportunities

Doing a comparison between the number of goals

scored and the percentage of goal opportunities the

Ri-one team was the team with the best performance.

This team achieved more than thirty-ﬁve goals with

only four percent of the total goal opportunities. In

the opposite side teams like FC Portugal needed seven

percent of goal opportunities to scored only four

goals. Analyzing the games it is easy to see that Ri-

One team had a simple attacking behavior, based on a

long pass to the team forwards that resulted in a sim-

ple goal against most of the teams.

5.2 Successful Passes

This statistic of the game must be improved for all

the teams in the near future. The values in it were

between eight to thirteen percent which is a very

low value compared, for example, with real football.

Teams use a very fast but not so safe playing style,

resulting in lots of ball losses when executing passes.

5.3 Regions Visited by the Best Teams

in the Tournament

The area of the ﬁeld more occupied by these teams

were the two midﬁelds area (our and their). Pecu-

liar is the behavior of the Ri-one team, which occu-

pied the penalties boxes areas rarely (compares with

other ﬁeld area). Other interesting observation was

the behavior of the Wright Eagle (WE) team, which

occupied the opponent left wing area less than one

percentage of their total

6 CONCLUSIONS AND FUTURE

WORK

This paper describes an approach to the use of a

data warehouse to extract knowledge from RoboCup

teams playing style. The transformations and data ex-

traction were developed in a very easy way by us-

ing the data warehouse, enabling to use the results

achieved to get information about the teams and their

playing style. With these results we concluded that

teams like TokyoTech or FC Portugal needed many

opportunities to score a goal in contrast with Ri-one

that scored most of their opportunities. An interesting

observation is that FC Portugal team has much more

goal opportunities in comparison with other teams,

but this team did not score many goals. So, this seems

like a parameter to improve in a near future by the FC

Portugal team, for example, shooting more to the goal

or developing simple but effective moves in the oppo-

nents area, to score goals.

The next developments in this project shall focus

two distinct areas. In the future the data warehouse

will have data not only from RoboCup 2006 Compe-

tition but also from previous tournaments. This point

will improve the knowledge about all the participating

teams, which will also enable to raise the performance

of a given Team in a near future. This point could also

allow to store a higher amount of data throw the use

of the developed data warehouse.

The other area of work, regards the implementa-

tion of new types of statistics in order to better char-

acterize the game.

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