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RoboSina from Scratch

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. Introduction Simulation System and Soccer Server Learning Algorithms used in RoboSina

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RoboSina from Scratch

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  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

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