1. RoboSina from Scratch Mostafa Rafaie-Jokandan & Nima KavianiBy
Mostafa Rafaie-Jokandan & Nima Kaviani
RoboSina from Scratch
Submitted as partial Fulfillment for the Requirements of the degree of
Bachelor of Science In
Software Engineering

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

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

4. Simulation System & Soccer Server
What is a Simulation System?
Why Simulation?
Soccer Server

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

6. Some of predefined parameters for Soccer Server

7. A Simulated Field

8. Models used in Soccer Server
Movement Model
Visual Model
Aural Model

9. Visual Model
(see ObjName Distance Direction DistChng DirChng BodyDir HeadDir)
ObjName ::= (p ”Teamname” UniformNumber goalie)
| (g [l|r])
| (b)
| (f c)
| (f [l|c|r] [t|b])
| (f p [l|r] [t|c|b])
| (f g [l|r] [t|b])
| (f [l|r|t|b] 0)
| (f [t|b] [l|r] [10|20|30|40|50])
| (f [l|r] [t|b] [10|20|30])
| (l [l|r|t|b])

10. Visual Model Visual Formulas Noise 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/π)
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

11. Visual Model 90 45

12. Aural Model
(hear Time Sender ”Message”)

13. Movement Model

14. Movement Noise Model

15. Actions for an Agent Catch Dash & Stamina Model Kick Tackle Turn Move
Turn-Neck
Point to
Focus
Say
Change View
Score

16. 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 didn’t have a coach?!!!

17. Learning Algorithms Definition Decision Trees
Artificial Neural Networks

18. Learning Algorithms-Keywords
Environment
Property
State
Goal
Learning Algorithm

19. Learning Algorithms-Keywords
State2
Propertyi
Goal State
State1
State3

20. Decision Trees Definition ID3 Basis ID3 Formulas ID3 Algorithm
What is C4.5

21. Decision Trees - Definition
A powerful tool for Inductive Inference
Proposing a model for discrete environments
Resistance against Noisy Data

22. in case of having a weather
Playing a Soccer Game
in case of having a weather
with conditions D ? ?
D=(
Outlook = Sunny
, Wind = Weak,
Humidity = High )

23. Decision Trees – ID3 Basis
Occam’s 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”

24. Decision Trees – ID3 Formulas

25. Decision Trees – ID3 Algorithm

26. Decision Trees – C4.5 A developed software on the basis of ID3
It’s 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

27. Neural Networks Definition A Mathematical Representation
BackPropagate Basis
BackPropagate Algorithm

28. Neural Networks-Definition
A mathematical model of human’s neurons
It’s abilities
Association, Clustering, Classification
Pattern Recognition
Generalization, Reference
Improvement
Classification on the basis of Learning Algorithm
Supervised
Unsupervised => Competitive

29. Neural Networks- Math Representation

30. 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

31. Neural Networks- BackPropagate Algorithm

32. Prominent Aspects System Architecture Localizing an Agent Intercept
Dribble
Formation
Defense System
Play With Ball System

33. System Architecture Describe the structure of the system's components
Functional Architecture
Operational Architecture
Implementation Architecture
Brief view to Functional Architecture

34. System Architecture-Functional Architecture

35. Localizing an Agent
What is localization? f

36. 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(m+n) for 2 polygons
T(n) = 2T(n/2) + O (n)
It is executed in O(nlogn) using divide and conquer for n polygons

37. Intercept As a skills As a strategy utility
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

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

39. Dribble
One , Two

40. Dribble
Mislead

41. Formation Usage New Works
Determine Agent position relative to the ball position
New Works
New Floating Formation
Adding A Free midfielder
Implementing the main formation as
Implementing forwards press.

42. New Floating Formation4 2 3 1
The formation is the main formation.

43. New Floating Formation4 4 2
The formation is a fully offensive formation.

44. Defense System
Skills
Block
Mark
Strategy
Offside line
Press

45. Defense System- Block

46. Defense System- Mark

47. Defense System- Offside Line
NO, offside!!!

48. NO, I lost the ball !!

49. Play With Ball System Coming soon !!!
It will be published within early days.

50. 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

51. 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

52. Failed (gx, gy) (bx, by) distcb dircb 1st Successful State
1st Successful State
State 19 =
State 24 =
Last Successful State
State 28 =

53. Shoot-Results Harms Very time consuming method!! Algorithm
Occurrence in 8 matches
Success Num
Success Percentage
Computational
141 93
65.9%
Learning
173
126
72.8%
Harms
Very time consuming method!!

54. 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

56. Pass-Results Harms Partial Observation causes problems
Pass Probability
Occurrence in 8 matches
Success Num
Success Percentage
85-100%
912
753
78.3%
70-85%
328
187
57.01%
55-70%
168 92
54.7%
Harms
Partial Observation causes problems
Body direction of the receiver is ignored during the train

57. 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

58. Conclusions & Suggestions

59. Acknowledgements

60. Thank you very much for being here
Any Question?
We are ready to Answer