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perft is a debugging tool, and the important thing is the number it returns, not the way you get it. If bulk counting makes debugging faster, I wonder what is wrong with it?

While Minic 0.45 is not released yet, here is a Minic no-book depth 30 opening line. What do you think of it ? the queen exchange seems to weaken white side :? [Event "Import"] [Site "https://lichess.org/jq3Ov8n1"] [Date "2019.03.30"] [Round "-"] [White "?"] [Black "?"] [Result "*"] [UTCDate "2019....

I think that first step if you have an engine should be to a build a test suite from games of the engine when the engine does not find the right move. Testing a new patch should be done first in 1000 positions that the engine failed to find the right move. Examining positions and games to discover ...

Thanks for the suggestion. It indeed works, modulo the deletion of the appropriate commands in the makefile. This is something I did not want to do at first because it feels as an alteration to the engine intended by the developer. You can also use the binary provided in the github repository. Avoi...

Also, several old engines have been compiled for Linux by Jim Ablett and are available here:

http://kirr.homeunix.org/chess/engines/ ... ENGINES%20++/

My programs run under Linux (better than under Windows in fact):
- Dumb 1.0 to 1.3 about 2200 Elo (CCRL 40/4 scale)
- Amoeba 1.0 to 2.8: about 2600 to 3000 Elo on CCRL 40/4
Have fun with them.

Hello, I made some further simplifications in Dumb's code, that resulted in a faster and slightly stronger chess engine. You can dowload some binaries here: https://github.com/abulmo/Dumb/releases/tag/v1.3 N.B.: I used more generic compilation settings. The 64 bit versions should work on any 64-bit ...

I did a fast test with Amoeba: - On tree size & search time. I did count the nodes & time searched at depth 18 on 200 positional positions: with aspiration: 674769965 nodes in 592.523s : 1138807 nps without: 654081365 nodes in 651.913s : 1003326 nps So aspiration window does not seem to reduce tree ...

I am not sure to understand your proposal. To me Reinforcement learning (RL) is what is often called here Texel Tuning, ie a way to tune the eval parameters from played game. Your proposal looks like building an opening book (from the current position) and loading it to the hash table to improve the...