Re: [Computer-go] CGOS source on github

2021-01-22 Thread Hiroshi Yamashita
Hi, The most noticeable case of this is with Mi Yuting's flying dagger joseki. I'm not familiar with this. I found Hirofumi Ohashi 6d pro's explanation half year ago in HCCL ML. The following is a quote. -

Re: [Computer-go] CGOS source on github

2021-01-22 Thread Rémi Coulom
Hi David, You are right that non-determinism and bot blind spots are a source of problems with Elo ratings. I add randomness to the openings, but it is still difficult to avoid repeating some patterns. I have just noticed that the two wins of CrazyStone-81-15po against LZ_286_e6e2_p400 were

Re: [Computer-go] CGOS source on github

2021-01-22 Thread David Wu
@Claude - Oh, sorry, I misread your message, you were also asking about ladders, not just liberties. In that case, yes! If you outright tell the neural net as an input whether each ladder works or not (doing a short tactical search to determine this), or something equivalent to it, then the net

Re: [Computer-go] CGOS source on github

2021-01-22 Thread Claude Brisson via Computer-go
Hi. Maybe it's a newbie question, but since the ladders are part of the well defined topology of the goban (as well as the number of current liberties of each chain of stone), can't feeding those values to the networks (from the very start of the self teaching course) help with large shichos

Re: [Computer-go] CGOS source on github

2021-01-22 Thread David Wu
On Fri, Jan 22, 2021 at 3:45 AM Hiroshi Yamashita wrote: > This kind of joseki is not good for Zero type. Ladder and capturing > race are intricately combined. In AlphaGo(both version of AlphaGoZero > and Master) published self-matches, this joseki is rare. >

Re: [Computer-go] CGOS source on github

2021-01-22 Thread David Wu
Hi Claude - no, generally feeding liberty counts to neural networks doesn't help as much as one would hope with ladders and sekis and large capturing races. The thing that is hard about ladders has nothing to do with liberties - a trained net is perfectly capable of recognizing the atari, this is

Re: [Computer-go] CGOS source on github

2021-01-22 Thread David Wu
On Fri, Jan 22, 2021 at 8:08 AM Rémi Coulom wrote: > You are right that non-determinism and bot blind spots are a source of > problems with Elo ratings. I add randomness to the openings, but it is > still difficult to avoid repeating some patterns. I have just noticed that > the two wins of

Re: [Computer-go] CGOS source on github

2021-01-22 Thread uurtamo
also frankly not a problem for a rating system to handle. a rating system shouldn't be tweaked to handle eccentricities of its players other than the general assumptions of how a game's result is determined (like, does it allow for "win" and "draw" and "undetermined" or just "win"). s. On Fri,