1. GA를 활용한 게임 에이전트 동작-CIG Competition PacMan- YonseiUniv Soft Computing Lab

2. 1/34 NOT YOUR FAULT!

3. 2/34 다음 중 GA 를 사용하여 풀기에 적합한 문제는? 7차 방정식 사용자 상태 추론 게임에서의 최적화 경로 찾기 하드웨어 설계 게임 전략 생성

4. 3/34 1. 탐색범위가 넓은 문제! 2. 반드시 제일 좋은 해를 구할 필요가 없는 문제!

5. 4/34 DO YOU BELIEVE IN GA?

6. 동기 5/34 실제 동물은 물리 환경에서 최적화된 움직임을 하고 있을까?

7. 문제 설계 6/34

8. 문제 설계(유전자) 7/34 Time(1.5 sec) Angular motor를 이용 각 관절의 각속도를 제어한다. Motor의 속도는 5단계(-2, -1, 0, 1, 2 )로 구성되어있다. 여기서 (-)값은 음의 방향의 각속도를 말한다. Joint Degree(38)

9. 유전자 평가 8/34 0 1 1 0 1 1 1 0 0 0 0 1 0 0 0 1 0 0 1 1 64 361 169 576 얼마나 전진했는가? 적합도 염색체

10. 실험결과 9/34

11. 확인 10/34 동영상!

12. 확인 11/34 이런 모양도!

13. CIG IEEE Symposium on Computational Intelligence and Games Tutorial Session Presentation Competition http://www.ieee-cig.org/competitions/ Competitions Chair: Simon M. Lucas 12/34

14. CIG Competition 13/34

15. Unreal Tournament Sponsored by 2K Australia The competition task is to create a computer game bot Indistinguishable from a human player major prize of A$7,000 cash Trip to 2K Australia's studio DeathMatch game type for the FPS Chatting will be disabled. Some aspects of the game play will be modified in a way not known to competitors. 14/34

16. TORCS Car Racing Daniele Loiacono, Julian Togelius and Pier Luca Lanzi Learn (or otherwise develop) a controller The distance raced in a fixed amount of time Best few controllers will race against each other Mac OS X, Windows and Linux, using Java and C++ Neural networks, Fuzzy logic, Force fields, Expression trees 15/34

17. Ms Pac-Man Non-deterministic game The world record for a human player currently stands at 921,360 Can anyone develop a software agent to beat that? the best software controller for the game of Ms Pac-Man Screen-capture to get the current state of the game 16/34

19. Loose in a fun, challenging way

20. No unintended weaknesses

21. No “golden path” to defeat

22. Must not look dumb

23. Must perform in real time (CPU)

24. Configurable by designers

25. Not hard coded by programmer

26. “Amount” and type of AI for game can vary

27. RTS needs global strategy, FPS needs modeling of individual units at “footstep” level

28. RTS most demanding: 3 full-time AI programmers

31. think of agent as NPC, enemy, ally or neutral

32. Loops through: sense-think-act cycle

35. Ex- tip-toe past, enemy doesn’t hear, but if run past, enemy hears (stealth games, like Thief)

36. Implement as event-driven

37. When player performs action, notify agents within range

38. Rather than sound reflection (complicated) usually distance within bounded area

39. Can enhance with listen attributes by agent (if agent is “keen eared” or paying attention)

40. Communication

41. Model sensing data from other agents

42. Can be instant (ie- connected by radio)

43. Or via hearing (ie- shout)

44. Reaction times

45. Sensing may take some time (ie- don’t have agent react to alarm instantly, seems unrealistic)

48. Finite State Machines, decision trees, … (FSM most popular, details next)

49. Appealing since simple, natural, embodies common sense and knowledge of domain

50. Ex: See enemy weaker than you?  Attack. See enemy stronger?  Go get help

51. Trouble is, often does not scale

52. Complex situations have many factors

53. Add more rules, becomes brittle

54. Still, often quite adequate for many AI tasks

56. Look ahead and see what move to do next

57. Ex: piece on game board (MinMax), pathfinding (A*)

58. Works well with known information (ie- can see obstacles, pieces on board)

59. Machine learning

60. Evaluate past actions, use for future action

62. May not be important in many games where agent short-lived (ie- enemy drone)

63. But if alive for 30+ seconds, can be helpful

64. ie- player attacks from right, so shield right

66. Many cases, easy to make agents dominate

67. Ex: FPS bot always makes head-shot

68. Dumb down by giving “human” conditions, longer reaction times, make unnecessarily vulnerable, have make mistakes

69. Agent cheating

70. Ideally, don’t have unfair advantage (such as more attributes or more knowledge)

71. But sometimes might “cheat” to make a challenge

72. Remember, that’s the goal, AI lose in challenging way

74. Base Environment http://cswww.essex.ac.uk/staff/sml/pacman/PacManContest.html Java Image Detector, Simulator, Game http://sclab.yonsei.ac.kr/courses/09AgentGame/Reference_Code.zip

75. 문제 설계 29/34 Sensing Thinking Acting IF then IF then If then . . . If then 상 하 좌 우

76. 문제 설계 30/34 1. 죽지 말자 2. 제일 가까운 고스트를 피하자 3. 고스트는 나를 기준으로 어디에 있는가? 4. 고스트의 반대방향으로!

77. 문제 설계 31/34 팩맨의 움직임 고스트의 위치 상 하 좌 상 하 좌 우 우 좌상 우상 좌하 우하 탐색범위? 256

78. 문제 설계 32/34 Evaluating! 문제를 잘 이해하는 게 관건! 많이 해보자!

79. 문제 설계 33/34 Expert Rule Based System(High Priority) GA Rule(Middle Priority) Expert Rule Based System(Low Priority)

80. 정리 34/34 1. GA 활용예 2. CIG Competition 소개 3. 게임의 범용 AI 설계법 소개 4. Pac_manAI 코드 소개 5. Pac_manAI를 위한 GA 기초설계