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Applying fuzzy control in fighting game ai

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These slides describe JerryMizunoAI (a.k.a. ChuMizunoAI) available in the following page:
http://www.ice.ci.ritsumei.ac.jp/~ftgaic/index-2h.html

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Applying fuzzy control in fighting game ai

  1. 1. Applying Fuzzy Control in Fighting Game AI Chun Yin Chu and Ruck Thawonmas Intelligent Computer Entertainment Lab. Graduate School of Information Science and Engineering Ritsumeikan University
  2. 2. In short… 1: Kaito Yamamoto, Syunsuke Mizuno, Chun Yin Chu and Ruck Thawonmas, "Deduction of Fighting-Game Countermeasures Using the k-Nearest Neighbor Algorithm and a Game Simulator," Proc. of 2014 IEEE Conference on Computational Intelligence and Games (CIG 2014), Dortmund, Germany, pp. 437–441, Aug. 26-29, 2014. • Applying fuzzy control in fighting game AI is able to alleviate the “cold start” problem of k-nn prediction technique1 • Fuzzy control is found to be more effective than crispy rules
  3. 3. What is Fighting Game? Board Game Turn-based, slow pace Well researched Fighting Game Real time, fast pace Emergent research field
  4. 4. FightingICE • First fighting game AI platform for annual competitions • Written in Java, suitable for AI development by small team • 3 rounds in a game • 60 frame/second • Data delayed by 15 frames • Imperfect information • Previously 2 categories • 1 Character • 3 Characters • Currently 1 character http://www.ice.ci.ritsumei.ac.jp/~ftgaic/index.htm
  5. 5. Existing techniques: k-nn Prediction & Simulation1 • AI is re-trained for each game • Use a game simulator to predict the current game state to counter the delay in information • Use k-nn for predicting the opponent’s next attack action (skills) among 31 possible candidates • Use another game simulator to decide a counter-measure action among a set of major actions 1: Kaito Yamamoto, Syunsuke Mizuno, Chun Yin Chu and Ruck Thawonmas, "Deduction of Fighting-Game Countermeasures Using the k-Nearest Neighbor Algorithm and a Game Simulator," Proc. of 2014 IEEE Conference on Computational Intelligence and Games (CIG 2014), Dortmund, Germany, pp. 437–441, Aug. 26-29, 2014.
  6. 6. Performance of k-nn Prediction & Simulation AI (MizunoAI) • MizunoAI is able to defeat the champion of the 2013 competition • Score > 500 is considered victory in a round Score of MizunoAI vs T, the 2013 champion
  7. 7. Cold Start • MizunoAI is re-trained for each game • If there is not enough opponent action data (=at the beginning of the game)… • No data to predict opponent’s action! The AI just keeps guarding! • MizunoAI is thus weak during the early stage of the fight => cold start Cannot predict and simulate!
  8. 8. Applying Fuzzy Control Is the number of opponent data sufficient? sufficient Use kNN prediction and simulation kNN prediction simulation …… Use rule-base. Distance? insufficient close far Use close- range attack Use long-range attack Fuzzy Fuzzy
  9. 9. Fuzzy Control – Use k-nn prediction? Singleton Defuzzification: Probability(“use_MizunoAI”) = sufficient(oppData) e.g. if oppData == 10, the probability of MizunoAI being used is 0.5 0 0.25 0.5 0.75 1 0 5 10 15 20 Membership oppData (number of opponent data) Membership function for "sufficient data" and "insufficient data" insufficient sufficient RULE 1 : IF oppData IS sufficient THEN decision IS use_knn; RULE 2 : IF oppData IS insufficient THEN decision IS rule_base; oppData is the number of opponent action record in the corresponding dataset
  10. 10. Fuzzy Control – close or far? Only the horizontal distance of the two characters is considered Singleton Defuzzification: Probability(“close-range-attack”) = close-range(distance); e.g. if distance == 60, the probability of using close-range attack is 0.5 RULE 1 : IF distance IS close THEN decision IS close_range_attack; RULE 2 : IF distance IS far THEN decision IS long_range_attack; 0 0.2 0.4 0.6 0.8 1 0 40 80 120 160 200 240 280 320 360 Membership distance Membership function for “close distance" and “far distance” close-range long-range
  11. 11. Evaluation • 200 3-round games (100 games played as P1 + 100 games as P2) against: • MizunoAI: original k-nn prediction and simulation AI as proposed by Yamamoto et al. (2014) • ChuMizunoAI_crispy: AI proposed by this paper, but replaced fuzzy control with crispy threshold • All AIs used 3 as the k-value for the k-nn algorithm • All AIs used the character KFM and fought on FightingICE v.1.02 Round 1 Round 2 Round 3 Average 542 502 514 SD 104 125 128 Results 129 wins 3 draws 68 loses 67 wins 70 draws 63 loses 83 wins 52 draws 65 loses VS ChuMizunoAI_crispy Threshold: oppData = 10, distance = 60 Round 1 Round 2 Round 3 Average 647 572 542 SD 129 166 146 Results 174 wins 26 loses 71 wins 110 draws 19 loses 56 wins 119 draws 25 loses VS MizunoAI • Better performance than the original MizunoAI • Fuzzy control fared better than crispy rule MizunoAI performed poorly due to cold start Fuzzy rule-base outperforms crispy rules
  12. 12. Video: ChuMizunoAI vs MizunoAI P1: ChuMizunoAI P2: MizunoAI
  13. 13. Fighting Game AI Competition 2015 Fighting Game AI Competition will be held at CIG 2015 Midterm Deadline: 7 June 2015 Final Deadline: 16 August 2015 Please refer to http://www.ice.ci.ritsumei.ac.jp/~ftgaic/
  14. 14. Questions are welcome Thank you for your kind attention
  15. 15. Appendix 1 – Fuzzy Rule-Based Move Sets STAND_B STAND_D_DB_BA THROW_A close, to-ground STAND_D_DF_FC/A STAND_D_DB_BB AIR_D_DB_BB far, to-ground STAND_F_D_DFA CROUCH_FB STAND_F_D_DFB close, to-air STAND_D_DF_FB AIR_D_DF_FA AIR_D_DB_BB far, to-air to-air to-ground close far
  16. 16. Appendix 2 – On Singleton Defuzzification • All fuzzy controls use singleton defuzzication • The membership values are directly used as probabilities, which is then used to derive a single defuzzified output 0 0.25 0.5 0.75 1 0 5 10 15 20 Membership oppData Membership function for "sufficient data" and "insufficient data" insufficient sufficient oppData = 6 P(“use_mizunoAI”) : P(“rule_base”) = 1 : 9 oppData = 13 P(“use_mizunoAI”) : P(“rule_base”) = 8 : 2
  17. 17. Appendix 3 – Evaluation using ChuMizunoAI_crispy with different parameters Round 1 Round 2 Round 3 Average 518 517 510 SD 57 47 62 Results 12 wins 1 draw 7 loses 8 wins 7 draws 5 loses 7 wins 6 draws 7 loses Threshold: oppData = 5, distance = 40 • 20 3-round games for each set of parameters (10 as P1, 10 as P2) • All against ChuMizunoAI_crispy • All figures are corrected to nearest integer Round 1 Round 2 Round 3 Average 549 507 501 SD 90 78 97 Results 14 wins 1 draw 5 loses 8 wins 5 draws 7 loses 9 wins 5 draws 6 loses Threshold: oppData = 5, distance = 60 Round 1 Round 2 Round 3 Average 542 523 544 SD 101 93 104 Results 14 wins 6 loses 8 wins 5 draws 7 loses 9 wins 6 draws 5 loses Threshold: oppData = 5, distance = 80
  18. 18. Appendix 3 – Evaluation using ChuMizunoAI_crispy with different parameters Round 1 Round 2 Round 3 Average 540 517 518 SD 78 64 78 Results 14 wins 3 draws 3 loses 6 wins 10 draws 4 loses 8 wins 10 draws 2 loses Threshold: oppData = 10, distance = 40 Round 1 Round 2 Round 3 Average 546 506 511 SD 97 60 85 Results 12 wins 8 loses 8 wins 7 draws 5 loses 8 wins 6 draws 6 loses Threshold: oppData = 10, distance = 80
  19. 19. Appendix 3 – Evaluation using ChuMizunoAI_crispy with different parameters Round 1 Round 2 Round 3 Average 517 525 502 SD 84 39 90 Results 12 wins 1 draws 7 loses 8 wins 10 draws 2 loses 7 wins 8 draws 5 loses Threshold: oppData = 15, distance = 40 Round 1 Round 2 Round 3 Average 501 507 504 SD 56 70 96 Results 11 wins 9 loses 7 wins 9 draws 4 loses 7 wins 9 draws 4 loses Threshold: oppData = 15, distance = 60 Round 1 Round 2 Round 3 Average 517 516 528 SD 99 62 123 Results 13 wins 7 loses 11 wins 4 draws 5 loses 8 wins 6 draws 6 loses Threshold: oppData = 15, distance = 80

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