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Playing games also throws out huge chunks of the search tree.

Yes, it does, but with each move the horizon is pushed out farther.

IMO game play (especially with learning of past results) is a more reliable testing method that trying to do search from the root position, even with a very strong engine or at long time control. I think there are many cases where it is difficult to get the "truth" of an opening from such a search. ...

Arasan has "permanent brain" (keeps a file of positions/scores for later use). It only works in xboard mode presently. It is effective. It does give a positive ELO gain in matches. But it is not magic and it won't boost Arasan's score way above engines that are already well above its strength. If an...

I think quite a few engine players on playchess are also active on InfinityChess.

I couldn't get the InfinityChess client software to work on my system. I downloaded it and the installer doesn't run.

--Jon

Permanent hash would have a similar effect.

Newest T60 Leela network gives eval of -73% for after 1 Million node search (a bit over a minute on 2080ti). If 1.g4 is losing it's interesting to ponder if openings like Benoni or Mar del Plata King's Indian variation are losing as well. If you play around a bit you get around 65% for white in tho...

The book was "seeded" with a set of a-priori move statistics. So not starting from zero. My idea was I could use this as a book that added some learning (both online and offline) to what the book currently had. Since the book already is decently tuned, adding very short-time control games or shallow...

Sounds like a weak move selection algorithm, or not using all the knowledge you have available, or too high expectation of the learning rate, or a combination of all three. I'm kind of curious what was the time controls you were using? I think this plays a huge role in learning rate per unit time s...

I can't even get the InfinityChess app to properly install on WIndows 10. And I assume if you are not on Windows, you are out of luck.

--Jon

A while back I was experimenting with a book learning system, and for a while it was selecting a number of "bad" moves including 1. g4. What I found was that even with poor opening choices the draw rate with Stockfish (self-play) was very high. This kind of screwed up the learning because poor choic...