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Computers and Killall-Go
Computers and Killall-Go
Computers and Killall-Go
Computers and Killall-Go
Computers and Killall-Go
Computers and Killall-Go
Computers and Killall-Go
Computers and Killall-Go
Computers and Killall-Go
Computers and Killall-Go
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Computers and Killall-Go

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  • 1. KILLALL GO & other Govariants- Main focus of these slides: kill-all go- Other topics briefly: - One-color-go ? Too easy for humans ? - Blind-go 19x19 ? Organizing a game ? (needs money) - random-go 19x19 or 9x9 ? - batoo-like variants: placing N initial stones.
  • 2. KILLALL GOPrinciple:- black has plenty of handicap stones,- but black wins if and only if he kills every white stone.- free handicap placement <== no algorithm works on this; good challenge- maybe good first step before Batoo (Batoo: both players set up initial stones)==> can be simulated by classical go interfaces by adjusting komi/handicap
  • 3. HANDICAP PLACEMENT PROPOSED BYSHI-JIMRemark: we have seen in the past thatMCTS is not good for choosing handicapplacement.
  • 4. HANDICAP PLACEMENT PROPOSED BY SHI-JIMFOR 13x13 H8MoGo evaluation with 50 000 sims/move: white wins.(more precisely, meta-MoGo: we play entire games withMoGo)
  • 5. HANDICAP PLACEMENT PROPOSED BY SHI-JIMFOR 13x13 H9MoGo evaluation with 50 000 sims/move: black wins.Ping-Chiang comment: not sure.==> we might try ?Remark:- mogos estimate on the opening: white wins.- but if games are played, black wins.==> usual phenomenon: MCTSBad for evaluating openings (meta-level requested).
  • 6. HANDICAP PLACEMENT PROPOSED BY SHI-JIMFOR 9x9 H4MoGo evaluation with 500 000 sims/move: black wins.
  • 7. HANDICAP PLACEMENT PROPOSED BY SHI-JIMFOR 9x9 H3MoGo evaluation with 500 000 sims/move: white wins.Ping-Chiang agrees.
  • 8. HANDICAP PLACEMENT PROPOSED BY SHI-JIMFOR 7x7 H2MoGo evaluation: not doneColdmilks code: 30% for black.
  • 9. HANDICAP PLACEMENT PROPOSED BY SHI-JIMFOR 19x19 H17MoGo evaluation with 50 000 sims/move: white wins
  • 10. CONCLUSIONS ON GO9x9 killall GO: should we conclude:- H3 too easy for white,- H4 too easy for black13x13 killall GO: H9 the right equilibrium point ? Interestingly, estimate by Meta-MCTS different from estimate by MCTS.19x19 killall GO: H17 known as a correct equilibrium (classical exercise), but for MCTS its easy for white. CONCLUSIONS ON MCTS- The fact that MCTS poorly estimates opening positions is interesting.- Maybe variants of Batoo ? Games in which: - each player sets N stones - then, game as usual ==> OT will launch some experiments; CAN WE DO NICE RESEARCH BASED ON THIS ?- Games against Ping-Chiang (H2 in 7x7: computer won as white, lost as black; H13: computer white wins H8 and loses H9)- Bandits for handicap placement- Remark: do we know Batoo players who would accept funny games on variants of Batoo ?

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