1. Robotic Soccer

2. Outline History Motivation Comparison with real soccer Artificial Intelligence in Robotic Soccer Simulation of Robotic Soccer

3. What is Soccer Robot? “A soccer robot is a specialized autonomous robot and mobile robot that is used to play variants of soccer” -Wikipedia

4. Robots Playing Soccer The idea of robots playing soccer was first mentioned by Professor Alan Mackworth (University of British Columbia, Canada) in a paper entitled “On Seeing Robots” presented at VI-92, 1992.

5. History Independently, a group of Japanese researchers organized a Workshop on Grand Challenges in Artificial Intelligence in October, 1992 in Tokyo, discussing possible grand challenge problems. This workshop led to a serious discussions of using the game of soccer for promoting science and technology.

6. Cont.. Factors Considered in the discussion: Technology feasibility. Social impact assessment. Financial feasibility.

7. Result As a result of these studies, it has been concluded that the project is feasible and desirable. In June 1993, a group of researchers, including Minoru Asada, Yasuo Kuniyoshi, and Hiroaki Kitano, decided to launch a robotic competition, tentatively named as “Robot J-League” Within a month, on receiving overwhelming reactions from researchers outside of Japan, renamed the project as the Robot World Cup Initiative, “RoboCup” for short.

8. The Year 1997 In the history of artificial intelligence and robotics, the year 1997 will be remembered as a turning point. In May 1997, IBM Deep Blue defeated the human world champion in chess. Forty years of challenge in the AI community came to a successful conclusion. On July 4, 1997, NASA’s pathfinder mission made a successful landing and the first autonomous robotics system, Sojourner, was deployed on the surface of Mars.

9. Together with these accomplishments, RoboCup made its first step towards the development of robotic soccer with an aim of beating human World Cup champion team by the year 2050. RoboCup and its aim

10. Motivation Why all this focus letting robots play games of soccer?  Dealing with the real world properties.  The element of competition is not only an interesting research question, it also serves as a way to put the research to the test.  Element of Competition.

11. Motivation Cont.  Standard problems.  Research on games, chess in particular, has generated significant advances in the theory of search algorithms and search control, as well as motivating cognitive studies into the ways that humans approach the same problems

12. Comparison Distributed CentralControl IncompleteCompleteInfo. accessibility Real timeTurn takingState Change DynamicStaticEnvironment SoccerChess

13. Lessons from real Soccer The problem of creating a team strategy from individual behavior  Performance is probably very bad on skills like passing and shooting.  Aritificial Intelligence.

14. Solutions for good team work in human soccer  Game system  Positioning  Offensive Positioning  Defensive Positioning  The golie

15.  Goalie

16. Others Freezing the game Positioning at special kick situations

17. Tactics  Simplicity  Let the ball work  Only Stop when one cannot advance immediately  Be receptible for a pass  Make passes playable for the receiving robot.

18. Tactics Cont..  Don’t be to obvious in your intentions.  Camouflage your weaknesses.  Move towards a passed ball.  Shield the ball of the opponent.  Do not slide unless absolutely necessary.  Be a good team player.

19. AI in Robotic Soccer Three divisions Machine Vision Camera Snapshots + Image Processing etc. Artificial Intelligence Logic of players movements. Communication Signal Transmission etc.

20. AI in Robotic Soccer Some Basic Definitions Roles Modularity Layered Operations Decision Matrix

21.  A decision matrix is a list of values in rows and columns that allows an analyst to systematically identify, analyze, and rate the performance of relationships between sets of values and information.

22. AI in Robotic Soccer Artificial Intelligence Layers Strategy layer Path planning layer Fuzzy reactive layer Motor control layer

23. Strategy Layer Strategy layer Operations Task identification Role prioritization Role assignment Robot destination assignment

24. Role Assignment Using weighted importance of roles Role allocation Heuristic

25. Task Identification & Role prioritization Inputs Ball speed towards own goal Ball distance from own goal Large, Multi-dimensional decision matrix Outputs Decision Matrix Heuristic weights of importance of the goalie, attacker role, defender role.

26. Robot destination assignment Based on role stereotype

27. Robot destination assignment Based on role stereotype Goalie Movement between the goal and the position of the ball but within the goal area. Ball moving toward the goal action Zonal division of goal area

28. Robot destination assignment

29. Based on role stereotype Defender Common Role Interchangeable role Similarity with Goalie’s target destination

30. Robot destination assignment

31. Based on role stereotype Attacker Aggressive Behavior Evasive Behavior Similarity with Goalie’s target destination

32. Merits & Demerits Operations satisfactory in simulation Inflexibility of fixed arrays to future additions Lack of sensible interpolation policy for unknowns Constructing, storing and accessing in decision matrix Inflexibility of this approach

33. FUZZY Algorithms Fuzzification Fuzzy Input Sets Fuzzy Output Sets Fuzzy Rules & Association memory matrix Defuzzification

34. Fuzzification Fuzzy set pair (U,m) where U is a set and sets whose elements have degrees of membership. Classical set and Fuzzy set Input variables are mapped into fuzzy sets “Process of converting a crisp input value to a fuzzy value”

35. Fuzzy Input Sets

37. Fuzzify ball speed and ball position. Example: Fuzzy Input for Fuzzy ball speed = - 0.25 Fuzzified Ball Speed = { 0.5, 0.5, 0 }

38. Fuzzy Output Sets Zero, Small Positive, Medium Positive, Large Positive, Very Large Positive

39. Fuzzy Output Sets one Fuzzy Output Value representing each of the three robot roles Output = { Attacker Fuzzy Output Set, Defender Fuzzy Output Set, Goalie Fuzzy Output Set, }

40. Fuzzy Rules & Association memory matrix Fuzzy Rules are very natural and can be expressed in natural Language. Example: If ball speed is negative and ball position is away then attacker priority is very large and defender priority is zero and goalie priority is small. If ball speed is zero and ball position is away then attacker priority is large and defender priority is small and goalie priority is medium.

41. Fuzzy Rules & Association memory matrix Fuzzy Association Memory Matrix (FAMM) is a complete mapping of all the fuzzy rules in our controller. Example

42. Defuzzification Necessity of defuzzifying our Fuzzy Output Values. Aggregation procedure v i is the centre value for the rule that corresponds to weight w i.

43. Path Planning Layer Inputs are outputs from Strategy Layer. A* implementation Fuzzy logic and A* navigation challenge Remedy: Create a supplementary algorithm to generate a map of robot soccer environment. Extensive refinement to ensure compatibility of A* with Fuzzy logic system.

44. Path Planning Layer Red bot is expected to move along the dotted path. Supplementary Algorithm generates the Map. A* Algorithm generates the points.

45. Fuzzy Reactive layer Inputs are outputs from Path Planning Layer Calculates angular velocity and forward speed for the bot

46. Motor control layer Inputs are outputs from Fuzzy reactive Layer Calculates the left motor velocity and right motor velocity Informs the communication module to transmit the required data to the correct bot.

47. Soccer Simulation Evaluation of various multi-agent systems and cooperative algorithms. Components :-  Soccer Server  Client

48. Soccer Server o Provides Virtual soccer field o Simulates the movements of players and the ball. o client-server communication

49. Modules Simulator module Message-board module Referee module

50. Overview

51. Selection of Play-plans Situation: Two attackers attempting to score a goal against a single opponent.

52. 1) The offensive player shoot the ball at the goal. 2) Pass the ball to his teammate. Here it’s a binary choice. but in general, there are a very large number of possible play situations, including many where both play plans may appear equally good. Possibilities

53. Experiment  The three players are placed in the penalty area randomly.  Ball placed on a line parallel to touchlines at a dist 1 m from the main attacker.  Main Attacker’s Behavior  Collect information about the positions of all objects.  Input the information to a Selector module.  Choose either a “pass” or a “shoot” according to the output of the selector module.

54. Defender is programmed to move toward and, if possible, to kick, the ball. The attacker’ s teammate is programmed to wait for a pass and, if the pass comes, to shoot at the goal.

55. Types of selectors 1) Neural Network trained on a sample set of data using back-propagation. 2) Apply the Decision tree learning procedure to the same data set.

56. Neural Network Eight inputs, which corresponded to the eight values of the distance and the angle from the main attacking player to his teammate, the defender, the center of the goal, the ball.  30 hidden units.  2 output units, Opass and Oshoot. All fully connected.

57. Selection  The pass plan is chosen with probability Opass /(Opass + Oshoot)  The shoot plan is chosen with probability Oshoot/(Opass + Oshoot).

58. Training Create a random database of 1000 situations. For each situation, we then chose, at random, whether to carry out a pass plan or a shoot plan and add the success or failure of a single simulation of this plan to the data item Apply Back-Propagation method to train the weights of the network.

59. Result

60. Output vs Direction of Opposite Player

61. On also varying the distance of opposite player

62. Decision Tree Method Decision Attributes Relative positions of the objects. Two classes “Success” and “failure”.

63. Steps 1) Data set 1 with positions of the objects and action = shoot. 2) Data set 2 with positions of the objects and action = pass. 3) Predict a class, success or failure, for each of the two data sets. 4) If only one plan is “success”, do the corresponding action. 5) If both classes are “success” do the action whose certainty factor is higher. 6) If both classes are “failure” pass or shoot randomly

64. Result

65. Conclusion  Soccer Server, a simulator of the game of soccer in which players, controlled by individual client programs, can play a soccer match  Demonstrated the potential of Soccer Server by reporting experiments that used the system to investigate how the selection of play plans could be learned  Research has been going in domain of Robotic Soccer with an aim of “defeating World Champion Human team by the year “2050”.

66. References  http://en.wikipedia.org/wiki/Soccer_robot http://en.wikipedia.org/wiki/Soccer_robot http://www.robocup.org/about-robocup/a-brief- history-of-robocup/ http://www.robocup.org/about-robocup/a-brief- history-of-robocup/ Artificial Intelligence in a multi agent robot soccer domain by “Remco Anthony Seesink” Applied Artificial Intelligence Journal: Soccer server: A tool for research on multiagent systems by “Itsuki Noda, Hitoshi Matsubara, Kazuo Hiraki & Ian Frank” Artificial Intelligence in Robot Soccer by “A. P. Gerdelan” supervised by “Dr. N. H. Reyes”

67. Thank You