RoboSina from Scratch

1. By 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? • 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 • 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

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 • 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 45 90

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. State2 Goal State State3 Learning Algorithms-Keywords Propertyi State1

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. 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 • Asupervised 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. f Localizing an Agent • What is localization?

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 • 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 • 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.

42. 4 3 2 1 New Floating Formation The 4-2-3-1 formation is the main formation.

43. 4 4 2 New Floating Formation The 4-2-4 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. NO, offside!!! Defense System- Offside Line

48. NO, I lost the ball !!

49. 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

50. 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