# Bayesian Ranking: From Xbox Live to Computer Go - PowerPoint PPT Presentation

Bayesian Ranking: From Xbox Live to Computer Go

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Bayesian Ranking: From Xbox Live to Computer Go

## Bayesian Ranking: From Xbox Live to Computer Go

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1. Bayesian Ranking:From Xbox Live to Computer Go Ralf Herbrich and Thore Graepel

2. Overview • Motivation: Ranking in Video Games • Bayesian Player Ranking: TrueSkill™ • Skill Belief, Likelihood, Update Equation • Applications in Online Gaming • Numerical Results on Halo 2 data • Ranking Moves: Computer Go • Conclusion

3. Motivation • Microsoft is the leader in online video gaming (Xbox Live). • Centralised game-independent service. • Gamercard, Achievements, TrueSkill™, etc. • What makes playing online games fun? • Good network connection (Broadband). • Seamless setup (Xbox Live). • Competitive matches (Ranking!).

4. Ranking in Video Games • Problem Setting: • k teams of n1,…,nk many players compete. • The outcome is a ranking between the teams (including draws). • Questions: • Skill si of each player such that • Global ranking between all players. • High quality of match between k teams.

5. Overview • Motivation: Ranking in Video Games • Bayesian Player Ranking: TrueSkill™ • Skill Belief, Likelihood, Update Equation • Applications in Online Gaming • Numerical Results on Halo 2 data • Ranking Moves: Computer Go • Conclusion

6. The Bayesian Approach • Classical logic deals with certain statements. • In the real world, uncertainty is abundant. • Degree of Belief (logic: 0% or 100%). “Player i’s skill is between 35 and 40” and “Player j’s skill is between 30 and 35” “Player i won against Player j” Bayesian Approach = Probability for Logic under Uncertainty P(A|B) = P(B|A) P(A) / P(B) Posterior Prior Likelihood Evidence

7. Overview • Motivation: Ranking in Video Games • Bayesian Player Ranking: TrueSkill™ • Skill Belief, Likelihood, Update Equation • Applications in Online Gaming • Numerical Results on Halo 2 data • Ranking Moves: Computer Go • Conclusion

8. TrueSkill™: Skill Belief • Track two numbers per player: • μ = Average skill of player • σ = Uncertainty about skill of player • Benefits: • Faster Skill Learning • Better Matchmaking • Accurate Prediction of Game Outcomes σ Belief in Skill Level μ 10 15 20 25 30 35 40 Skill Level

9. TrueSkill™: Likelihood • Likelihood: • Game outcomes are all permutations including draws between pairs of teams. • Latent performance model for players: P(game outcome|s1,…,sn)= P(ti’s are in game outcome order| s1,…,sn) • Team performance, ti, is sum of players’ performances in the team.

10. Likelihood Example: Two Players 7 0.2 Player 2 wins 0.18 6 Players 1 and 2 draw 0.16 5 0.14 0.12 4 Performance of Player 2 Probability density 0.1 3 0.08 0.06 2 Players 1 and 2 draw 0.04 Player 1 wins 1 0.02 Player 2 wins Player 1 wins 0 0 0 1 2 3 4 5 6 7 -8 -6 -4 -2 0 2 4 Performance of Player 1 - x x 1 2

11. 3rd Place 1st Place 2nd Place TrueSkill™: Skill Updates Game Outcome Belief in Skill Level 0 10 20 30 40 50

12. Overview • Motivation: Ranking in Video Games • Bayesian Player Ranking: TrueSkill™ • Skill Belief, Likelihood, Update Equation • Applications in Online Gaming • Numerical Results on Halo 2 data • Ranking Moves: Computer Go • Conclusion

13. The True Skill System: Applications • Leaderboard: • (Conservative) skill estimate : μ- 3·σ • Matchmaking: • Competitive game = Fun game! • Match quality = Probability of a draw • Team Balancing: • Maximise match quality by greedy search.

14. 0.1 0.1 0.08 0.08 0.06 0.06 Probability density Probability density 0.04 0.04 0.02 0.02 0 0 -5 -5 0 0 5 5 10 10 15 15 20 20 25 25 30 30 Skill Skill 0.8 0.7 0.6 0.5 Probability density 0.4 0.3 0.2 0.1 0 -5 0 5 10 15 20 25 30 Skill TrueSkill™: Matchmaking Lobby Possible Matches ?

15. Overview • Motivation: Ranking in Video Games • Bayesian Player Ranking: TrueSkill™ • Skill Belief, Likelihood, Update Equation • Applications in Online Gaming • Numerical Results on Halo 2 data • Ranking Moves: Computer Go • Conclusion

16. Results: Halo 2 Multiplayer Beta • 5 different “hoppers” • Free-For-All: 60261 games (5946 players) • 1 vs. 1: 6240 games (1672 players) • 5 maps, 3 different game variants. • Matchmaking was relaxed to level gap 9. • Parameters in all experiments: • Performance variation factor: 60% • Draw Probability: 5% • Dynamics variation factor: 2%

17. Free-For-All: char vs. SQLwildman? 40 35 30 25 Level 20 15 ) char ( TrueSkill 10 ) SQLwildman ( TrueSkill char (Halo 2) 5 SQLwildman (Halo 2) 0 0 100 200 300 400 Number of games played

18. Free-For-All: char vs. SQLwildman? 100% char wins SQLwildman wins 80% Both players draw 60% Winning probability 40% 20% 5/8 games won by char 0% 0 100 200 300 400 500 Number of games played

19. Near Normal Level Distribution – 650,000 Players TrueSkill™ Analysis of Halo 2 (Nov. – Dec. 2004) 50 40 30 Level 20 10 1 3 2.5 2 1.5 1 0.5 0 Level Occupancy 4 x 10 x 10

20. TrueSkill™ • Skill based ranking instead of experience based ranking for better matchmaking. • TrueSkill™ system is • a generalisation of ELO • tracks a belief distribution • can deal with multiple team/players/draws • Every Xbox 360 Live game uses TrueSkill™ ranking & matchmaking!

21. Overview • Motivation: Ranking in Video Games • Bayesian Player Ranking: TrueSkill™ • Skill Belief, Likelihood, Update Equation • Applications in Online Gaming • Numerical Results on Halo 2 data • Ranking Moves: Computer Go • Conclusion

22. Liberty: Fast Pattern Based Computer Go • Go: Simple rules yet very complex game. • Computer Go: • Infancy (best programs at weak amateur level). • Problems: Evaluation and Branching factor (≈250). • New grand challenge of AI (replacing Chess). • Idea: Learning good moves from expert play. • Applications: • Reduce branching factor for search. • Fast pattern based Go engine.

23. Pattern Urgency Table σ Belief in Urgency … μ 10 15 20 25 30 35 40 Urgency From Local Patterns to Probability of Moves =25,  = 5.2 Not in database! Black to move

24. Harvesting and Learning • Two processes: • Harvesting patterns. • Ranking patterns. • We learn the value of these patterns using a modified Bayesian TrueSkill™ ranking system. • Partial ranking from every expert move: • Move made wins over any other move available on the board. • Nothing is known about the ranking of the un-played moves.

25. Better than State-of-the-Art 1 Liberty (120K games) Liberty (20K games) Liberty (20K games) Werf et al. (2002) 0.8 0.6 55% in top 5 cumulative probability 0.4 32% top 0.2 0 1 5 10 15 20 25 30 expert move rank

26. Error increases towards end of the game Better Prediction Early

27. Later Stage Early Stage Bigger Patterns Early

28. Error decreases for increasing pattern size Bigger Patterns  Better Predictions

29. Bayesian Ranking for Go • Bayesian ranking makes full use of the information available from expert moves. • Simple features used in the approach already beats state-of-the-art prediction methods. • Approach is ideal for server-side Go AI: • Very fast at move selection time. • Large memory footprint. • Planned extension to 1,000,000 game records and context-aware patterns.

30. Conclusions • Bayesian Ranking is a powerful technique • TrueSkill™generalises ELO and has a large influence on the online gaming experience of Xbox gamers. • Provides a principled and efficient way for learning the value of local patterns in the game of Go.