Since you’re here...

We hope you will consider supporting us today. We need your support to continue to exist, because good entries are more and more work time. Every reader contribution, however big or small, is so valuable. Support "Chess Engines Diary" even a small amount– and it only takes a minute. Thank you.
============================== My email: jotes@go2.pl



Chess engine: Expositor 2BR17

-

Expositor is a UCI-conforming chess engine for AMD64 / Intel 64 systems.
Author: Kade  Rating CEDR 15'+10"=3048, Rating CEDR 3'+3"=2994

v.2BR17:
Overview
This release features two major improvements:

Search Tuning Many of the constants in Expositor's search have now been tuned with a variation of simultaneous perturbation stochastic approximation (SPSA). Along with internal iterative reductions, this has increased the strength by approximately 100 Elo against 2WN29 in self-play.

Internal Tablebases Expositor now has an internal 3-man tablebase and I'm working on an optional 4-man tablebase. This will not increase her strength, since her endgame heuristics are sufficient for 3- and 4-man endgames (besides some KBN v K positions), but I wanted a break from work on HCE bootstrapping and thought it would be fun.

The complete list of changes made in this release is below.

Fixes
fixed error where spurious moves could be printed at the end of the principal variation (PV)
fixed error in Linux build script that caused the script to fail when building commits with commit dates in August through December

Improvements
search constants have been tuned with a modified form of SPSA
added internal iterative reductions
transposition table entries are now prefetched
added internal 3-man tablebase
threads now clear their own context and statistics in parallel when a search begins
the PGN parser can now handle moves written without a space after the move number (e.g. 1.e4)

Time Control
target time adjustments based on PV stability now depend on the depth at which the PV changes
searches are now less likely to stop immediately after the PV changes

Miscellaneous
scores taper to half their nominal value rather than to zero as the 50-move horizon approaches
made slight improvements to the efficiency of static exchange analysis
made various small simplifications in the codebase
added various developer tools

Individual statistics: Expositor 2WQ23 - 72 games (+ 31,= 14,- 27), 52.8 % (15'+10")
Jackychess 0.9.14             :   1 (+  1,=  0,-  0), 100.0 %
Kouri 1.15                    :   1 (+  1,=  0,-  0), 100.0 %
Sapeli 2.0                    :   1 (+  1,=  0,-  0), 100.0 %
Rustic Alpha 3.0.0            :   1 (+  1,=  0,-  0), 100.0 %
Iathena 2020-12-19            :   1 (+  1,=  0,-  0), 100.0 %
FracTal 1.0                   :   1 (+  1,=  0,-  0), 100.0 %
Chareth 0.1.0                 :   1 (+  1,=  0,-  0), 100.0 %
Quokka 2.1                    :   1 (+  1,=  0,-  0), 100.0 %
Monochrome r203               :   1 (+  1,=  0,-  0), 100.0 %
Stockfish 14.1                :   2 (+  0,=  1,-  1), 25.0 %
Pulse 1.7.3                   :   1 (+  1,=  0,-  0), 100.0 %
SugaR AI 2.50                 :   2 (+  0,=  1,-  1), 25.0 %
Fisherov 0.98i                :   2 (+  0,=  0,-  2),  0.0 %
BLANK 1.3.0                   :   1 (+  1,=  0,-  0), 100.0 %
Lynx 0.9.0                    :   1 (+  1,=  0,-  0), 100.0 %
MisterQueen                   :   1 (+  1,=  0,-  0), 100.0 %
Lc0 0.28.2                    :   4 (+  0,=  1,-  3), 12.5 %
Weiawaga 4.0                  :   1 (+  1,=  0,-  0), 100.0 %
Popochin 4.3                  :   1 (+  0,=  1,-  0), 50.0 %
Prophet 4.1                   :   1 (+  1,=  0,-  0), 100.0 %
Anka 0.6.3                    :   1 (+  1,=  0,-  0), 100.0 %
Eman 7.80                     :   2 (+  0,=  0,-  2),  0.0 %
Dragon 2.6.1                  :   2 (+  0,=  1,-  1), 25.0 %
ClassicAra 0.9.9              :   5 (+  2,=  3,-  0), 70.0 %
SlowChess 2.83                :   2 (+  0,=  0,-  2),  0.0 %
Leorik 1.0                    :   1 (+  1,=  0,-  0), 100.0 %
Fatalii 0.1.0                 :   1 (+  1,=  0,-  0), 100.0 %
Tantabus 2.0.0                :   1 (+  1,=  0,-  0), 100.0 %
Koivisto 7.14 Ipman           :   2 (+  0,=  1,-  1), 25.0 %
Journeyman 1.0                :   1 (+  1,=  0,-  0), 100.0 %
Eggnog 3.0                    :   1 (+  1,=  0,-  0), 100.0 %
Corchess 250222               :   2 (+  0,=  0,-  2),  0.0 %
Stockfish 010322              :   2 (+  0,=  1,-  1), 25.0 %
Frozenight 1.0.0              :   2 (+  2,=  0,-  0), 100.0 %
Rebel 14.2                    :   2 (+  0,=  0,-  2),  0.0 %
Stockfish 250222 Ivec         :   2 (+  0,=  0,-  2),  0.0 %
Dumb 1.9                      :   2 (+  2,=  0,-  0), 100.0 %
BrainLearn 16                 :   2 (+  0,=  1,-  1), 25.0 %
Lozza 2.2                     :   1 (+  1,=  0,-  0), 100.0 %
Little Goliath 3.15.4         :   1 (+  0,=  1,-  0), 50.0 %
Frozenight 2.0.0              :   1 (+  1,=  0,-  0), 100.0 %
Princhess 0.7.0               :   1 (+  1,=  0,-  0), 100.0 %
Walleye 1.6.0                 :   1 (+  1,=  0,-  0), 100.0 %
CorChess 010422               :   2 (+  0,=  0,-  2),  0.0 %
ProteusSF 006                 :   2 (+  0,=  1,-  1), 25.0 %
Stockfish 010422 Ivec         :   2 (+  0,=  0,-  2),  0.0 %
Kayra 1.3                     :   2 (+  0,=  1,-  1), 25.0 %


github:https://github.com/expo-dev/expositor/releases/tag/2BR17


Comments

Post a Comment

Popular posts from this blog

New Strong Engines Test, by Chess Engines Diary, 2024.04.12

-
Image
  💾  552 (!) games from the tournament download   👍 @chessenginesdiary  Country - Poland, City -  Malbork 🕓 Time 3'+3" 💻HP Pavilion i5-1035G1 8GB RAM 🖬 GUI-Banksia All  CEDR  317.321 games download  (01.04.2024 - 3'+3")  HypnoS 030424, Yuli GM Pro 16, Stockfish 16.1 and ShashChess 35 . These four engines scored the same number of points and placed at the top of the table in this strong tournament. Tech table: Engine KN/move NPS dep/mov time/mov mov/game time/game fails Alexandria 6.1.0 3218 576429 30.2 5.6 56.7 316.2 Berserk 13 4779 841025 36.7 5.7 56.0 318.1 Brainlearn 28 2275 366977 31.4 6.2 43.5 269.4 Caissa 1.18 5492 923486 30.2 5.9 50.4 299.7 Clover 6.1.19 4803 854377 31.4 5.6 62.2 349.4 Cool Iris 12.10 2201 356730 27.6 6.2 51.2 315.5 CorChess 20240331 2253 372774 27.4 6.0 52.2 315.8 Crystal 8 3582 571994 20.3 6.3 47.7 298.8 Fire 9.3 5225 876565 22.0 6.0 55.5 331.1 Fisherov chess monk 1.2 3487 619728 36.0 5.6 60.4 340.0 Hyp
Read more

New chess opening book: M11.2 (bin and ctg)

-
Image
  Book - CTG format Customers who wish to use the CTG format book will need a GUI capable of reading this format, ie. Chessbase GUIs.  Please save the book in your book folder. We strongly recommend that you "Optimize" the book in the Book options dialog so the tournament moves are chosen. The book is very wide even in tournament mode unlike most other books. A type of book format mainly used by chess programs from Chessbase , such as Fritz, Chess Base, Komodo, FatFritz, Houdini , etc. Book - BIN format (  Used by chess GUIs Arena, Banksia and Droidfish) Binary books have some important advantages, especially for computer chess engines: space-efficient, fast on accessing and searching, more useful information.  At the most simple form, they are just a conversion of above text-opening books into binary, in the ready form for software and save text-binary converting steps. However, the major number of top books is built and worked with hash values from transpositions. Often ea
Read more

New version chess engine: Lc0 BT4

-
Image
LcZero - CEDR Rating=3683 vBT4: Big transformer 4. New network architecture which builds off of BT3 by adding two types of auxiliary heads, future heads and categorical value heads. The categorical value heads predict a distribution over values of q rather than a WDL outcome distribution, and the future heads predict the moves that will be played over the next two plies. The hope is that these heads will give additional information to the net to improve training speed. We've also fixed half-precision training, so this model will be larger. BT4 training started in mid-October and is expected to take a few months. It has 15 layers with 1024 embedding size, 32 heads per layer, and 1536 emb size, for roughly a doubling in size over BT3.  Individual statistics:  Lc0 0.30.0 Stockfish 16 5/13 -3 13 Games Booot 7.2 6/10 +2 10 Games Critter 1.6a 8/9 +7 9 Games Dragon 3.2 4/8 +0 8 Games Stockfish 20230729 3.5/8 -1 8 Games ShashChess 33.2 3.5/8 -1 8 Games Fisherov chess monk 1.2 3/
Read more

Dragon NNUE by Komodo Chess - it's free!

-
Image
    Dragon by Komodo Chess Larry Kaufman: We have released Dragon at komodochess.com. Dragon uses NNUE (Neural Network Updated Efficiently) technology, originally developed for the game of shogi. Komodo has a great deal of chess knowledge in its evaluation. Training an NNUE network based on this evaluation was both an advantage and a challenge, requiring experimentation with architectures and data generation of billions of positions. The reinforcement learning phase for Dragon is in its infancy, but is already showing great promise. Dragon is a huge strength improvement over 14.1, the last release, about 197 elo in MCTS mode and 189 elo in standard mode, at CCRL blitz time control on one thread based on direct matches. With four threads, the gains were 156 elo in MCTS and 170 in standard mode. The improvement is due to the embedded neural network providing much more accurate evaluation and also in some sense gaining an extra ply or so of search by “seeing” some tactics that a normal ev
Read more