1. KILLALL GO & other Go
variants
- 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 GO
Principle:
- 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 BY
SHI-JIM
Remark: we have seen in the past that
MCTS is not good for choosing handicap
placement.

4. HANDICAP PLACEMENT PROPOSED BY SHI-JIM
FOR 13x13 H8
MoGo evaluation with 50 000 sims/move: white wins.
(more precisely, meta-MoGo: we play entire games with
MoGo)

5. HANDICAP PLACEMENT PROPOSED BY SHI-JIM
FOR 13x13 H9
MoGo evaluation with 50 000 sims/move: black wins.
Ping-Chiang comment: not sure.
==> we might try ?
Remark:
- mogo's estimate on
the opening: white wins.
- but if games are played,
black wins.
==> usual phenomenon: MCTS
Bad for evaluating openings
(meta-level requested).

6. HANDICAP PLACEMENT PROPOSED BY SHI-JIM
FOR 9x9 H4
MoGo evaluation with 500 000 sims/move: black wins.

7. HANDICAP PLACEMENT PROPOSED BY SHI-JIM
FOR 9x9 H3
MoGo evaluation with 500 000 sims/move: white wins.
Ping-Chiang agrees.

8. HANDICAP PLACEMENT PROPOSED BY SHI-JIM
FOR 7x7 H2
MoGo evaluation: not done
Coldmilk's code: 30% for black.

9. HANDICAP PLACEMENT PROPOSED BY SHI-JIM
FOR 19x19 H17
MoGo evaluation with 50 000 sims/move: white wins

10. CONCLUSIONS ON GO
9x9 killall GO: should we conclude:
- H3 too easy for white,
- H4 too easy for black
13x13 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 it's 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 ?