RoboSina from Scratch

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RoboSina from Scratch
By Mostafa Rafaie-Jokandan Nima Kaviani

• Submitted as partial Fulfillment for the
  Requirements of the degree of
• Bachelor of Science
• In
• Software Engineering

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• Introduction
• Simulation System and Soccer Server
• Learning Algorithms used in RoboSina
• Prominent Aspects of RoboSina
• RoboSina Agent and Learning
• Suggestions and Conclusion

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Introduction

• Why RoboCup?
• An approach to Distributed Artificial
  Intelligence
• DAI vs CAI
• Where are we moving toward?
• Who were we in RoboSina?

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Simulation System Soccer Server

• What is a Simulation System?
• Why Simulation?
• Soccer Server

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Soccer Server

• Match Rules
• Controlled by Soccer Server
• Controlled by Human
• Properties of the Simulated Pitch
• A 105.0 68.0 field covered by flags
• Goal widths are twice a real one

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Some of predefined parameters for Soccer Server
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A Simulated Field
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Models used in Soccer Server

• Movement Model
• Visual Model
• Aural Model

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Visual Model

• (see ObjName Distance Direction DistChng DirChng
  BodyDir HeadDir)
• ObjName (p Teamname UniformNumber goalie)
• (g lr)
• (b)
• (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)

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Visual Model

• Visual Formulas
• prx pxt - pxo
• pry pyt - pyo
• vrx vxt - vxo
• vry vyt - vyo
• Direction arctan(pry/prx) - ao
• erx prx/Distance
• ery pry/Distance
• DistChng (vrx erx) (vry ery)
• DirChng (-(vrx ery) (vry erx))/Distance
  (180/p)
• BodyDir PlayerBodyDir - gentBodyDir -
  AgentHeadDir
• HeadDir PlayerHeadDir - AgentBodyDir -
  AgentHeadDir
• Noise Formulas
• d Quntize ( exp(quantize(log(d) . quantize
  step )), 0.1)
• Quantize (V , Q) ceiling(V/Q) Q

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Visual Model
45
90
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Aural Model

• (hear Time Sender Message)

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Movement Model
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Movement Noise Model
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Actions for an Agent

• Catch
• Dash Stamina Model
• Kick
• Tackle
• Turn
• Move
• Turn-Neck
• Point to
• Focus
• Say
• Change View
• Score

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• Heterogeneous Players
• Do you know how a body builder differs a normal
  human?
• Acting in an environment with heterogeneous
  creatures covers all possible properties
• Coach
• Mapping of DAI to CAI with restricted bandwidth
  in communication
• What was the role of Jose Morinio in a team if we
  didnt have a coach?!!!

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Learning Algorithms

• Definition
• Decision Trees
• Artificial Neural Networks

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Learning Algorithms-Keywords

• Environment
• Property
• State
• Goal
• Learning Algorithm

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Learning Algorithms-Keywords
Propertyi
State1
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Decision Trees

• Definition
• ID3 Basis
• ID3 Formulas
• ID3 Algorithm
• What is C4.5

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Decision Trees - Definition

• A powerful tool for Inductive Inference
• Proposing a model for discrete environments
• Resistance against Noisy Data

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Playing a Soccer Game in case of having a
weather with conditions D
?
?
Outlook Sunny
D(
, Wind Weak,
)
Humidity High
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Decision Trees ID3 Basis

• Occams Razor, mid of 14th century
• It is vain to do with more what can
• be done with less entities should
• not be multiplied beyond necessity

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Decision Trees ID3 Formulas
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Decision Trees ID3 Algorithm
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Decision Trees C4.5

• A developed software on the basis of ID3
• Its abilities
• Avoiding over fitting the data
• Reduced error pruning
• Rule post-pruning
• Handling continuous attributes
• Handling attribute with different costs
• Choosing an appropriate selection measure
• Handling training data with missing attribute
  values
• Improving computational efficiency

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Neural Networks

• Definition
• A Mathematical Representation
• BackPropagate Basis
• BackPropagate Algorithm

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Neural Networks-Definition

• A mathematical model of humans neurons
• Its abilities
• Association, Clustering, Classification
• Pattern Recognition
• Generalization, Reference
• Improvement
• Classification on the basis of Learning Algorithm
• Supervised
• Unsupervised gt Competitive

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Neural Networks- Math Representation
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Neural Networks- BackPropagate Basis

• A supervised learning algorithm
• Works on the basis of revising Weight Values
• Training Example (x, t) where x represents the
  entry, and t is the expected output value
• ai(l) representes the output gerenrated by the
  network in the layer named l
• x ai(0)
• m is the frequency for the execution of learning
  algorithm

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Neural Networks- BackPropagate Algorithm
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Prominent Aspects

• System Architecture
• Localizing an Agent
• Intercept
• Dribble
• Formation
• Defense System
• Play With Ball System

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System Architecture

• Describe the structure of the system's components
• Functional Architecture
• Operational Architecture
• Implementation Architecture
• Brief view to Functional Architecture

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System Architecture-Functional Architecture
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Localizing an Agent

• What is localization?

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Localizing an Agent

• A new approach used
• It deals with points rather than edges
• Uses a swipe line to find active edges
• A point will be added if
• It is a result of an intersection
• It is surrounded by both active edges of the
  other polygon
• Stops after reaching the end point of a polygon
• It is executed in O(mn) for 2 polygons
• T(n) 2T(n/2) O (n)
• It is executed in O(nlogn) using divide and
  conquer for n polygons

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Intercept

• As a skills
• Intercept the ball in at any distance.
• As a strategy utility
• Determine ball owner
• Predict ball owner
• Determine who must be intercept ball.
• Need to communication to enhance its efficiency

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Dribble

• Running with the ball from one point to another
  as quickly as possible
• Misleading obtrusive defender
• Holding ball if it is need .

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Dribble

• One , Two

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Dribble

• Mislead

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Formation

• Usage
• Determine Agent position relative to the ball
  position
• New Works
• New Floating Formation
• Adding A Free midfielder
• Implementing the main formation as 4-2-3-1
• Implementing forwards press.

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New Floating Formation

• The 4-2-3-1 formation is the main formation.

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New Floating Formation

• The 4-2-4 formation is a fully offensive
  formation.

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Defense System

• Skills
• Block
• Mark
• Strategy
• Offside line
• Press

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Defense System- Block
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Defense System- Mark
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Defense System- Offside Line
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Play With Ball System

• Coming soon !!!
• It will be published within early days.

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RoboSina Agent Learning

• Agent learns to Shoot
• Neural Network and training a shoot skill
• Using BP and Matlab
• Agent learns to Pass
• Decision Trees and training a pass skill
• Using ID3 and C4.5

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Learning how to Shoot

• Problem Definition
• Mapping the environment to a discrete model
• 6 input entries
• 6 output entries
• Grabbing relevant properties
• Using trainer to collect training samples
• Choosing the proper target to shoot
• Making the appropriate Neural Network
• Modeling the network with matlab
• A 2 layer network
• tgsig as activation function in the first layer
• logsig as activation function in the second layer
• Applying training samples to the network
• Results

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Failed
(gx, gy)
(bx, by)
distcb
dircb
1 0 0 0 1 1
bx by gx gy distcb dircb
1st Successful State
State 19 100111
State 24 110001
State 28 111001
Last Successful State
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Shoot-Results

• Harms
• Very time consuming method!!

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Learning how to Pass

• Problem Definition
• Grabbing relevant properties
• Using trainer to collect training samples
• Making the appropriate Decision Tree
• using C4.5 to create the tree
• Creating pass.names file
• applying training samples to the tree
• Creating pass.data file
• Results

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Pass-Results

• Harms
• Partial Observation causes problems
• Body direction of the receiver is ignored during
  the train

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RoboSina Project

• Honors
• Championship of the 3rd US-Open RoboCup
  Competitions, May 2005, Atlanta, USA
• 2nd place of the 3rd Iranian Open RoboCup
  Competitions, April 2005, Tehran, Iran
• 5th place of RoboCup World Cup 2004, Lisbon,
  Portugal
• Championship of the 2nd US-Open RoboCup
  Competitions, May 2004, New-Orleans, USA
• Championship of the 2nd Iranian Open RoboCup
  Competitions, March 2004, Tehran, Iran
• 10th place RoboCup World Cup 2003, Padova, Italy

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Conclusions Suggestions
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Acknowledgements
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Thank you very much for being here

• Any Question?
• We are ready to Answer