RoboCup

1
RoboCup
By Paul Marlow Date February 17, 2002
2
Background

• Initially started as the J-League (Japan Robot
  Soccer League)
• 1993, several American researchers became
  interested bringing about the Robot World Cup
  Initiative (RoboCup)
• The first games and conferences took place in 1997

3
Background II

• Goal was to provide a new standard problem for AI
  jokingly called The life of AI after Deep
  Blue
• RoboCup differs by focusing on a distributed
  solution rather than centralized
• The RoboCup Federation was started to coordinate
  the research through workshops, conferences and
  yearly competitions

4
Goals

• Stated as By mid-21st century, a team of fully
  autonomous humanoid soccer players shall win the
  soccer game, comply with the official rules of
  the FIFA, against the winner of the most recent
  World Cup
• Right now, the various leagues consist of either
  robots or programs, which cooperate in order to
  defeat the opponent team

5
Simulation League

• Provides a platform to develop software
  techniques, without the necessity of creating
  physical robots
• Consists of three main applications
• Soccer Server
• Soccer Monitor
• Soccer Player(s) - agents

6
Soccer Server

• A system allowing several autonomous program
  agents to play a virtual soccer game
• The games are carried out in a client/server
  style where each client one player
• The communication used is UDP/IP, and can
  therefore be used over a network connection, or
  even the Internet

7
Soccer Monitor

• Used to display the visual progress of the game
• Several Monitors can be connected to the server
• It can also be used to interrupt game play by
  doing simple tasks such as dropping a ball
• The latest version of the Soccer Server / Monitor
  is 8.03 5.24 was the last version for Windows

8
Autonomous Players

• Are the brains of the players
• Receive sensory information from the server, upon
  which decisions are made
• Commands are formatted and sent to the server
  using UDP sockets

9
Rules Judged by the Automated Referee

• Kick-Off
• Goal
• Out of Field
• Player Clearance
• Play-Mode control, including offsides
• Half-time and Time-up

10
How to Start the Server

• Download and install applications (running the
  configure and make scripts for the Unix / Linux
  systems)
• Run the Server (default host is localhost and
  default port is 6000)
• Run the Monitor, connecting to the host and port
  of the Server
• Connect the players also to the Server host and
  port

11
Connection Communication Protocols

• From client to server
• (init TeamName (version VerNum) (goalie))
• (reconnect TeamName Unum)
• (bye)
• From server to client
• (init Side Unum PlayMode) as a response for both
  client init and reconnect messages
• Side l r Unum 1 11

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Client Control Protocols
Client Command Once per Cycle
(catch Direction) Yes
(change_view Width Quality) No
(dash Power) Yes
(kick Power Direction) Yes
(move X Y) Yes
(say Message) No
(sense_body) No
(score) No
(turn Moment) Yes
(turn_neck Angle) Yes
13
Client Sensor Protocol - Hear

• (hear Time Sender Message)
• Sender online_coach_left/right, referee, self,
  Direction
• Direction -180 180 degrees

14
Hear Example

• (hear 18 self FCPortugal ETV 18 world_status r 2
  0.98 -10.95 16.64 1 0.95 0.00 0.00 1 0.95 0.00
  0.00 3 0 OtGL- - fGXtZ 3F.- /sdhAl 1p.- 0 40.0
  0.0 0 2 )

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Client Sensor Protocol - See

• (see Time ObjInfo)
• ObjInfo
• (ObjName Distance Direction DistChange DirChange
  BodyFacingDir HeadFacingDir) or
• (ObjName Distance Direction DistChange DirChange)
  or
• (ObjName Distance Direction) or
• (ObjName Direction)

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Client Sensor Protocol See II

• ObjName
• (p TeamName UniformNum goalie) or
• (b) or
• (g lr) or
• (f c) (f lcr tb) (f p lr tcb)
• (f g lr tb) (f lrtb 0)
• (f tb lr 1020304050)
• (f lr tb 102030)
• (l lrtb)
• (B) (F) (G) (P)

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Client Sensor Protocol See III

• See protocol has changed from version 5 (Windows)
  to version 8 (Unix/Linux).
• The Windows version differs in the ObjName
  portion. Instead of using the first letter as
  the ObjName, the older versions use the fully
  qualified name
• b ball, B Ball, l line, L Line, p
  player, P Player, f flag, F Flag, g goal,
  G Goal

18
See Example

• (see 18 ((f r t) 44.7 -22) ((f g r b) 47.9 30)
  ((g r) 44.7 22) ((f g r t) 42.5 13) ((f p r c)
  30.3 34 -0 0) ((f p r t) 25.3 -7 0 0) ((f t r 40)
  36.2 -37) ((f t r 50) 44.7 -29) ((f r 0) 49.4 20)
  ((f r t 10) 47 8) ((f r t 20) 46.5 -3) ((f r t
  30) 48.4 -15) ((f r b 10) 53.5 30) ((f r b 20)
  59.1 38) ((f r t) 44.7 -22) ((f g r b) 47.9 30)
  ((g r) 44.7 22) ((f g r t) 42.5 13) ((f p r c)
  30.3 34) ((f p r t) 25.3 -7 0 0) ((f t r 40) 36.2
  -37) ((f t r 50) 44.7 -29) ((f r 0) 49.4 20) ((f
  r t 10) 47 8) ((f r t 20) 46.5 -3) ((f r t 30)
  48.4 -15) ((f r b 10) 53.5 30) ((f r b 20) 59.1
  38) ((p "FCPortugal") 36.6 28) ((l r) 41.7 -89))

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Client Sensor Protocol Sense_body

• (sense_body Time
• (view_mode high low narrow normal
  wide)
• (stamina StaminaEffort)
• (speed AmountOfSpeed DirectionOfSpeed)
• (head_angle HeadAngle)
• (kick KickCount)
• (dash DashCount)
• (turn TurnCount)
• (say SayCount)
• (turn_neck TurnNeckCount)
• (catch CatchCount)
• (move MoveCount)
• (change_view ChangeViewCount))

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Sense_body Example

• (sense_body 19 (view_mode high normal) (stamina
  4000 1) (speed 0 0) (head_angle 0) (kick 0) (dash
  0) (turn 0) (say 98) (turn_neck 0))

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Krislet

• There does not exist many clients that run under
  the old Windows environment. Those that do, tend
  to connect to a Unix/Linux computer running the
  Server
• Krislet is an extremely dumb program, which can
  be difficult to beat as all the players run
  after the ball
• Krislet however, can communicate using the later
  protocols, and therefore all the parsing is
  complete

22
Stripslet

• Written by Aloke Wiki
• Based off of Krislet
• A Stripslet implementation is made up of four
  main concepts Actors, Sensors, Actions, and a
  GoalList

23
Actors

• These are designed to implement a specific
  action, such as run-to-ball or score-goal
• Contains two main elements, an execute method
  providing the functionality, and a name which
  is used as a key into a hash table.
• To add a new Actor
• Add a new class to the Actor.java, implementing
  the Actor Interface
• Add the new Actor to the ActorTable hash in
  StripsBrain.java

24
Sensors

• These consist of objects which include a sense
  method, as well as a name for a hash table key.
• Each is designed to determine the boolean outcome
  of a specific predicate, such as can-see-goal.
• To add a new Sensor
• Add a new class to Sensor.java, implementing the
  Sensor Interface
• Add the new Sensor to the SensorTable hash in
  StripsBrain.java.

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Actions

• Actions consist of four members name,
  precoditionList, addList, deleteList.
• name corresponds directly to that of the Actor
  name property (e.g. score-goal)
• precoditionList is a list of predicates which
  must be true before execution of the action (e.g.
  have-ball can-see-goal)
• addList is a list of predicates that will be true
  upon execution of the action (e.g. ball-in-net)
• deleteList is a list of predicates that will be
  false upon execution of the action (e.g.
  have-ball)

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Actions II

• To add an Action
• StripsBrain maintains a list of actions, so any
  new actions must be added to this list
• Note that multiple predicates in a list are
  separated by spaces
• actionList.add(new Action("score-goal", // name
• "have-ball can-see-goal", // pre
• "ball-in-net", // add
• "have-ball")) // delete

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GoalList

• Contains a list of goals for which the agents try
  to achieve
• Currently the default list consists of only one
  predicate ball-in-net
• Interesting possibilities include
• Adding multiple goals
• Re-evaluating the goal list each cycle depending
  on the environment

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Approach

• The difference between our approach and those of
  others, is human supervision.
• The goal, is to provide a process with which one
  can create a symbolic log file as a
  representation of the socket communication during
  a game.
• In addition, use machine learning techniques on
  this data.

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Approach II

• Instead of learning from mistakes/success as
  reinforcement learning does, the human user will
  actually help classify the data to be learned.
• However, there are problems with this approach,
  in that it can be tedious (as will be shown).

30
Step 1 Obtain a Log File

• In order to learn, the communication between the
  client and server need to be logged to a file
  thus the reason for our Logger.
• It acts like a router, sitting in-between the
  agent and the soccer server.
• The agent(s) connect to the Logger, which
  establishes a connection to the Soccer Server,
  forwarding communication all the while logging
  also writing them to file(s).

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Step 1 Continued

• An entry in the log file will look something like
  the following
• (see 0 ((g r) 23.8 -19) ((f g r t) 27.1 -33) ((f
  g r b) 22.4 -2) ((f r t 10) 32.8 -33) ((f r 0)
  28.5 -16 0 0) ((f r b 10) 27.7 4 -0 0) ((f r b
  20) 30 23) ((f r b 30) 35.2 38) ((p "EW_02" 1)
  22.2 -21 0 0 0 0) ((l r) 22.4 -89))

32
Step 2 - Classification

• Once the log file has been obtained, it must be
  used in order to classify the data into symbolic
  form.
• Our Classifier application provides the user with
  a GUI such that objects can be classified as the
  user deems appropriate.

33
Step 2 Continued
34
Step 2 Continued

• After all the classification required has been
  completed, use of the Generate button will create
  an ARFF file.
• The ARFF file is used in WEKA a machine
  learning repository as a data file.

35
Step 3 Data Mining

• For some aspects of soccer, the determination of
  a particular action depends on more than one
  player.
• The LogMiner is used to filter out unwanted data,
  and may be used in the future to obtain
  particular entries i.e. relating to passing.
• It makes use of the ARFF file(s) to convert the
  numerical data into a symbolic representation

36
Step 3 Continued

• The new log entry will look as follows
• (see 0 ((g r) 23.8 -19) ((f g r t) 27.1 -33) ((f
  g r b) 22.4 -2) ((f r t 10) 32.8 -33) ((f r 0)
  28.5 -16 0 0) ((f r b 10) 27.7 4 -0 0) ((f r b
  20) 30 23) ((f r b 30) 35.2 38) ((p "EW_02" 1)
  22.2 -21 0 0 0 0) ((l r) 22.4 -89))
• (see 0 (Goal Left_Near) (Flag Left_Near) (Flag
  Front_Near) (Flag ExtremeLeft_Far) (Flag
  Front_Near) (Flag Front_Near) (Flag Right_Far)
  (Flag Right_Far) (Teammate Left_Near) (Line
  ExtremeLeft_Near))

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Step 4 Use of Learning

• A GUI called IAS (In then Agents Shoes) was
  developed by Tarek Hassan, allowing a human to
  play as one of the agents.
• Instead of playing via commands, the goal is to
  convert the server communication to symbols
  using these for visualizing the objects.

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Step 4 Continued

• The problem, is that with any sort of symbolic
  representation, there will be a loss of detail
  therefore the objects displayed will not be exact
  just approximations.
• However, in real soccer, this is exactly the
  case! A player does not know that a teammate is
  20 metres ahead, and 22 degrees to the left.

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Summary of Process

• The process involves 4 main steps
• Obtain a log file (Logger)
• Classification of objects (Classifier)
• Data mining (LogMiner)
• Using learned material (Advanced IAS).

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References

• Most of the information about RoboCup itself was
  taken using from the RoboCup Soccer Server
  manual.
• For the latest manuals and code, visit the
  RoboCup project website at
• http//sourceforge.net/projects/sserver

41
Q A

• Feel free to ask any questions
• If time permits, I will attempt to run a
  demonstration game.

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Demo

• This small demo shows the three major components
• Soccer Server
• Soccer Monitor
• Soccer players (Krislet)