1. Blind GoBlind Go 1. Blind games & blind go 2. Optimal strategies in games 3. ExperimentsTAO, Inria-Saclay IDF, Cnrs 8623, Lri, Univ. Paris-Sud,OASE Lab,Korea,Summer 2011 1

2. Blind gamesWhat are blind games ?- Players dont look at the board (the board can beremoved, except for the referee)- They announce their moves (orally)- They should memorize the board, so that they make no mistake.Is it hard ?- Blind Chess is common among strong players.- Blind 9x9 Go is common among strong players.- Blind 19x19 Go is extremely hard (looks impossible but some players can do it)Two humans (at least) can play at a very strong levelin 19x19 blind Go: Bao Yun, Pierre Audouard.2 3. Blind games require memory Computers have memory. No problem. Humans have limited memory, but they can improve theencoding. ==> get strong at game X, and you will be much more able to memorize games of X. Known in Chess, widely believed in Go. Fundamental fact: positions extracted from real gamesare easier to memorize than random positions.3 4. Real positions...4 5. and random positions.5 6. Memorization test. Not enough subjects, not enough samples; yet, it suggests thatrandom positions are hard to memorize for Go players. Not surprising.6 Can we benefit from this in CI ? 7. Blind GoBlind Go 1. Blind games & blind go 2. Optimal strategies in games3. ExperimentsTAO, Inria-Saclay IDF, Cnrs 8623, Lri, Univ. Paris-Sud,OASE Lab,Korea,Summer 2011 7 8. Optimal playConsider a two player games: Max vs Min. Finite state space. Loops allowed.Either Max wins, or Min wins, or draw.Simple facts for full-information games:- Deterministic games: one of the two players always wins; or always draw. (you can guess who with Zermelo algorithm)- Stochastic games: there is a real number p such that, in case of perfect play, Expected reward = P(Max wins) + P(draw)/2 = arbitrary close to p. (Everett)- Partial information (deterministic):p undecidable (Auger et al, IJFCS 2012).This is for games between gods; ableof perfect play, with perfect memory. 8 9. Optimal play: realityConsider a two player games: Max vs Min. Finite state space. Loops allowed.Either Max wins, or Min wins, or draw.In real life, optimality depends on the opponent:==> in real life, computers sometimes choose bad moves(bad for computers, who are close to Gods in chess)against humans, if this leads to difficult situations (for humans). ==> iterated rock-paper-scissor: humans are poorrandom generators ==> computers can learn their bias. Blind games are equal to perfect information game (if no memory limit). What about reality ?9 10. Blind GoBlind Go 1. Blind games & blind go2. Optimal strategies in games3. ExperimentsTAO, Inria-Saclay IDF, Cnrs 8623, Lri, Univ. Paris-Sud,OASE Lab,Korea,Summer 2011 10 11. Play stupid moves ?Test in 6x6 blind Go.Computerplaysas well aspossible.Computerplaysstrange (bad)opening..11 12. Play stupid moves ?Test in 6x6 blind Go.Computerplaysas well aspossible.Computerplaysstrange (bad)opening..12 13. Blind GoBlind Go 1. Blind games & blind go2. Optimal strategies in games3. ExperimentsTAO, Inria-Saclay IDF, Cnrs 8623, Lri, Univ. Paris-Sud,OASE Lab,Korea,Summer 2011 13 14. Move 45 = human mistake dueGames against pros to blindfolded playFour games against 2 pros: P.-C. Chou (5P) C-H. Chou (9P)Two games won by computer.One game won by human.One game almost won by human...but lost because the humandid not remember the situation.Only one win for humans. 14 (game by P.-C. Chou 5P) 15. Ping-Chiang Chou (5P, 9x9expert) the loss of computerThis game is thegame lost by MoGoTWHuman said that thegame might have beenmuch more complicated;computer might havewon by playingcomplicated moves.General question: how to force complicated moves ?==> good for blind games==> good for bad situations(similar to playing simple15 moves in good situations) 16. Game won against C.-H. Chou 9Pin a non-blind-go mannerC.-H. Chou (9P) saidthat F6 was very goodmove.Blind-go or not,maybe this was a winanyway.16 17. nd2 game won against C.-H. Chou9P in a non-blind-go mannerC.-H. Chou (9P) saidthat white played agood manego-basedopening (symmetricmoves until the rightmoment).Blind-go or not,maybe this was a winanyway.17 18. Comments by prosWe need a visual support.An empty board is much better than noboard at all.==> we check with amateurs18 19. Blind 5x5 Go with and withoutempty boardDont resignearly !Dont resignearly !19 20. Blind 7x7 Go with and withoutempty board20 21. Summary Strange moves harder to memorize (as in Chess) ; ==> playing as in non-blind might be a bad idea. Computers vs Humans Dont resign early! Play unusual(in 19x19, computers are already unusual...) Computers stronger than humans in blind 9x9 Go ? 2/2 against 9P 1/2 against 5P What about 19x19: Commonly believed that computers are far weaker; Our only experiment is a win for computer with H4.(strange win, for sure) 21 22. ConclusionsNot enough Xps ==> XPs involving humans take time :-(Visual support helps (empty visual support!)Win with no handicap against pros in 9x9 ()Win with H4 against P. Audouard (big human mistake, due tounexpected move)Playing strange moves might be good==> e.g. game against P. Audouard==> tests against amateursRules: replay illegal moves (this rule makes endgames less trivial)22 23. Further workImportance of the quality of visual support/environment for blind games ?==> link with computer-aider pedagogy==> see e.g. caterpillar learningDo blind-games require very different abilities ?Beyond IQ ?Is blind-X a good training for X ? 23 24. Finished! Thanks for your attention ! ...24