Session at FOSSASIA 2017

1. Getting started with
Open Source Game AIs
Melvin Zhang
melvin@melvinzhang.net
@melvinzhangzy
Stockfish Pachi

2. https://en.wikipedia.org/wiki/File:ST Battle Chess.png

3. http://afflictor.com/2012/09/11/chess-programs-regularly-play-at-good-amateur-level/

4. https://en.wikipedia.org/wiki/Deep Blue (chess computer)
Deep Blue

5. https://stockfishchess.org/ Lang:C++ License:GPLv3
Stockfish

6. Game tree
https://en.wikipedia.org/wiki/Game tree

7. Optimal play
Terminal
min player
max player

8. Optimal play
1 01 1 1Terminal
min player
max player

9. Optimal play
1 01 1 1
0
Terminal
min player
max player

10. Optimal play
1 01 1 1
0 1
Terminal
min player
max player

11. Optimal play
1 01 1 1
0 1
1
Terminal
min player
max player

12. Chess has about 1046
(2153
) states!

13. Minimax algorithm with heuristic score
Cut-off
min player
max player

14. Minimax algorithm with heuristic score
.7 .1 .6 .9Cut-off
min player
max player

15. Minimax algorithm with heuristic score
.7 .1 .6 .9
.1
Cut-off
min player
max player

16. Minimax algorithm with heuristic score
.7 .1 .6 .9
.1 .6
Cut-off
min player
max player

17. Minimax algorithm with heuristic score
.7 .1 .6 .9
.1 .6
.6
Cut-off
min player
max player

18. https://tests.stockfishchess.org/
Testing AI changes is crucial

19. Heuristic scores are hard!

20. http://mathworld.wolfram.com/Go.html

21. by Google Deepmind
https://deepmind.com/research/alphago/

22. https://gogameguru.com/alphago-races-ahead-2-0-lee-sedol/

23. http://pachi.or.cz/ Lang:C License:GPLv2
Pachi

24. Monte Carlo evaluations
Cut-off
min player
max player

25. Monte Carlo evaluations
Cut-off
min player
max player

26. Monte Carlo evaluations
Cut-off
min player
max player

27. Monte Carlo evaluations
Cut-off
min player
max player

28. Monte Carlo evaluations
Cut-off
min player
max player
.7

29. Monte Carlo Tree Search (MCTS)

30. Some games have hidden information!

31. http://magic.wizards.com/en/events/coverage/gpsin15/father-son-2015-06-27

32. https://magarena.github.io Lang:Java License:GPLv3

34. Choose a random instantiation of the hidden
information during simulation

35. Theory Practice

36. Theory Practice
• Optimal play
• Minimax
• Monte Carlo
tree search
• Sampling hidden
information

37. Theory Practice
• Optimal play
• Minimax
• Monte Carlo
tree search
• Sampling hidden
information
Stockfish Pachi