MadChess 3.1 Beta 0c601ea (Singular Move)

I strengthened MadChess by extending the search horizon of singular moves. I gleaned the idea from the Stockfish chess engine.

Quoting from my Pull Request #17:

  • In the GetSearchHorizon method, added call to a new method, IsBestMoveSingular, that determines if best move that had failed high in recent searches is best by a significant margin.
  • If so, extend the search by one ply. A code comment explains why:
    • The best move (from the cache) is singular. That is, it’s the only good move in the position.
    • Evaluation of the current position relies on the accuracy of the singular move’s score.
    • If the engine misjudges the singular move, the position could deteriorate because no alternative strong moves exist.
    • To increase confidence in the singular move’s score, search it one ply deeper.

In addition, in Pull Request #16 I implemented asymptotic history scores. This simplifies keeping history scores within defined bounds. Also I fixed a bug in the Standard Algebraic Notation (SAN) parser for positions where the moving piece is disambiguated by square (not merely by rank or file). These changes improved code quality but did not increase playing strength.

Singular move increased the playing strength of MadChess 3.1 Beta by 13 ELO.

 

Feature Category Date Commit1 WAC2 ELO Rating3 Improvement
Singular Move Search 2021 Jun 14 0c601ea 290 2617 +13
Endgame Eval Scaling Evaluation 2021 Apr 08 4d22dec 286 2604 +12
Bishop Pair Evaluation 2021 Mar 14 2960ec9 285 2592 +22
Position Cache Optimization Search 2021 Feb 23 42d7702 286 2570 +8
Move Generation Optimization Search 2021 Feb 17 22002dc 287 2562 +12
PVS and Null Move Search 2021 Feb 09 f231dac 285 2550 +20
Remove Aspiration Windows Search 2020 Dec 20 4b7963b 290 2530 +9
Time Management Search 2020 Dec 19 d143bb5 286 2521 +8
Crash Bug Search 2020 Aug 29 2d855ec 288 2513 +0
King Safety Evaluation 2020 Aug 16 6794c89 288 2513 +63
Eval Param Tuning Evaluation 2020 Jul 23 bef88d5 283 2450 +30
Late Move Pruning Search 2020 Feb 08 6f3d17a 288 2420 +29
Piece Mobility Evaluation 2020 Feb 01 5c5d4fc 282 2391 +62
Passed Pawns Evaluation 2018 Dec 27 103 279 2329 +119
Staged Move Generation Search 2018 Dec 15 93 275 2210 +39
History Heuristics Search 2018 Dec 03 84 275 2171 +28
Eval Param Tuning Evaluation 2018 Nov 24 75 272 2143 +47
Sophisticated Search
Material and Piece Location
Baseline 2018 Nov 08 58 269 2096 0
  1. GitHub commit (hash) or Subversion source code revision (integer)
  2. Win At Chess position test, 3 seconds per position
  3. Bullet chess, 2 min / game + 1 sec / move

MadChess 3.0 Released

I have released version 3.0 of my chess engine. This is a complete rewrite of the engine using bitboards. I began the project two and a half years ago and worked on it sporadically, with long stretches of inactivity. I didn’t work on MadChess at all in 2019. Life got too busy, personally and professionally.

Gradually, I improved MadChess’ playing strength, surpassing the previous version, and crossing the 2600 ELO threshold. Considering MadChess 3.0 doesn’t have a sophisticated evaluation function, I’m satisfied to have reached that milestone. I’ll likely pursue evaluation improvements in a future version.

I have written MadChess 3.0, like its predecessors, in C#. I use preprocessor directives and Visual Studio solution configuration to include or exclude CPU intrinsics (PopCount and LeadingZeroCount) and target various platforms when compiling a binary executable: Windows 64 bit (x64) and Windows 32 bit (x86). When you click the 3.0 EXE link on the Downloads page, you’ll notice the ZIP file contains x64, x64 Non-PopCount, and x86 directories. Install the appropriate version for your computer’s CPU. The x64 binary is the strongest version of the engine.

MadChess 3.0 is built on Microsoft’s .NET 5 framework, which is open-source and cross-platform. On the Downloads page, I provide binaries only for Windows. However, .NET 5 supports Linux and Mac, so you’re welcome to compile it for those platforms. If you do attempt to compile a non-Windows binary, please let me know if you succeed or experience difficulties.

I have taken a minimalist approach to engine configuration. MadChess 3.0 doesn’t expose many UCI parameters. However, it does support UCI_LimitStrength and UCI_Elo, so you may configure MadChess for a more enjoyable game- assuming you’re not a Grandmaster.

Enjoy! Leave a comment on this blog post or click the Contact Me link and let me know what you think of my chess engine.

Tactical Minefield in Won Game

I played an interesting blitz game a couple nights ago against MadChess 3.0 Beta. The engine is strong enough for me to release it. Before I do, I’m improving features not related to maximizing engine strength. In fact, quite the opposite: I’m working on UCI_LimitStrength and UCI_Elo options that reduce the engine’s playing strength. This enables us mere mortals to configure MadChess for a more enjoyable game- competitive but with winning chances gifted to us by an engine purposefully playing inaccuracies and blunders.

The game began as follows. Playing white, I develop my pieces. MadChess 3.0 Beta mindlessly pushes a few pawns forward before belatedly developing its pieces. MadChess and I play the opening and middlegame inaccurately- including a significant blunder on my 6th move. (See analysis at end of this blog post.)

1.e4 e5 2.Nf3 h5 3.Be2 f5 4.Nc3 b6 5.Nxe5 a6 6.Ng6 Rh7 7.Nxf8 Kxf8 8.exf5 c5 9.Bxh5 d6 10.Nd5 Rh8 11.Qf3 Qd7 12.Nxb6 Qe7+ 13.Kd1 Bb7 14.Qg4 Nh6 15.Qg6 Be4

rn3k1r/4q1p1/pN1p2Qn/2p2P1B/4b3/8/PPPP1PPP/R1BK3R w - - 7 16

Here the game quickly gets very tactical. Keep in mind I’m playing MadChess 3.0 Beta at reduced strength. I’ve capitalized on its weak moves and have obtained a winning position. That my position is winning is not in doubt: White has won a bishop for a knight, has the bishop pair, and is up four pawns. However, MadChess’ defense complicates my winning position. Despite its moves being suboptimal, MadChess increases the chance a patzer like me spoils the position.

The game continues 16.Re1 Ra7 17.f3 {Another big blunder by me.} Qd8 18.Rxe4 Rf7 19.d3 Re7 20.Bg5 Nc6 21.Kd2 Qe8

4qk1r/4r1p1/pNnp2Qn/2p2PBB/4R3/3P1P2/PPPK2PP/R7 w - - 5 22

Here I miss a mate in two. Can you spot it? It’s easy when given the hint. In the game, with the clock ticking, I thought, “I have a strong attack here. My queen is leading the attack. I don’t want to swap it off.” Instead I play Qxd6, pinning black’s rook on e7. If black takes my bishop on h5, I thought I’d follow by moving my queen to the back rank to check the black king and win black’s other rook. I overlooked black’s knight on c6 covers my queen’s two infiltration squares (b8 and d8). Damn.

The game continues 22.Qxd6 Qxh5 23.Bxe7+ Nxe7 24.Nd7+ Kg8 25.Rxe7 Qg5+ 26.Kc3 Nf7 27.Qb8+ Kh7 28.Qg3

7r/3NRnpk/p7/2p2Pq1/8/2KP1PQ1/PPP3PP/R7 b - - 6 28

Now I’m willing to trade queens. But black isn’t. MadChess takes my rook. I sensed (correctly) it didn’t matter because of the strength of my attack, but I fail to press my advantage maximally.

The game continues 28… Qxe7 29.Qg6+ Kg8

6kr/3Nqnp1/p5Q1/2p2P2/8/2KP1P2/PPP3PP/R7 w - - 2 30

Here, under time pressure, I overlook a crushing move. Afterwards, when reviewing the game I saw it immediately. Nonetheless, I went on to win without making any more significant mistakes:

30.Qe6 Qd8 31.Re1 Rh7 32.Qe8+ Qxe8 33.Rxe8#

A very enjoyable game! I had MadChess 3.0 Beta do post-game analysis. I set it to full strength and asked it to identify moves where I erred by a pawn or more. You may review MadChess’ suggested improvements in the variations it added to the game below.

MadChess 3.0 Beta 4d22dec (Endgame Eval Scaling)

I improved MadChess 3.0 Beta’s detection of drawn endgames. The IsPawnlessDraw method scores the following positions as drawn. Though it continues to search moves for a swindle (opponent mistake that makes a drawn game winnable).

  • 2N vrs <= 1 Minor
  • Q vrs 2B
  • Q vrs 2N
  • Q vrs Q
  • Q vrs R + Minor
  • R vrs R + <= 1 Minor
  • Q vrs 2R
  • 2R vrs R + Minor
  • 2R vrs 2R

Testing revealed considering R vrs <= 2 Minors a draw increased evaluation error and caused the engine to play weaker. I left that endgame in the IsPawnlessDraw function as commented out code (explaining the regression) to thwart any temptation to add it later.

In addition, I added a DetermineEndgameScale method that scales down the score of difficult to win endgames.

  • Winning side has no pawns and is up by a bishop or less.
    • Winning side has a rook or more.
    • Winning side has less than a rook.
  • Sides have opposite colored bishops and no other pieces.
  • All other endgames are scaled by winningPawnCount.

Also, I added a GetTotalScore method that scales down the score as games approach a draw by 50 moves (100 ply) without a capture or pawn move.

Finally The GetStaticScore method brings together the entire evaluation calculation. See the Evaluation.cs source code file for full details. Here’s the code in simplified form.

These code changes increased the playing strength of MadChess 3.0 Beta by 12 ELO. MadChess has crossed the 2600 ELO threshold, at least at bullet chess (2 min / game + 1 sec / move). To date, I have tested MadChess 3.0 Beta exclusively at bullet time control. I was curious how MadChess would perform given more time per game. Of course I’d give its opponents equal additional time. Wouldn’t this benefit both engines equally? Well, chess engines do not scale equally with time. Would MadChess 3.0 Beta or its opponents benefit more from the additional time? Or would it be a wash? That is, they’d scale equally and MadChess 3.0 Beta would achieve the same blitz rating as bullet rating?

MadChess has crossed the 2600 ELO threshold at bullet time control.

It turns out, similar to previous versions, MadChess scales better per time than its opponents. Its blitz chess rating is 2638 ELO. I have started a tournament with rapid time controls (14 min / game + 7 sec / move), however, I won’t know the results for a month or so. Unlike bullet and blitz, I do not have a database of chess engine games at rapid time control. Therefore I cannot run a gauntlet tournament pitting MadChess 3.0 Beta against ten other engines with established ratings. I must run an all-play-all round robin tournament of 48 engines, including MadChess 3.0 Beta, to establish ratings.

MadChess scales better per time than its opponents. Its blitz chess rating is 2638 ELO.

My priority now is to ensure MadChess 3.0 Beta has feature parity (UCI_LimitStrength, MultiPV, etc) with the last release of MadChess, 2.2. Once that’s complete, I’ll release MadChess 3.0.

 

Feature Category Date Commit1 WAC2 ELO Rating3 Improvement
Endgame Eval Scaling Evaluation 2021 Apr 08 4d22dec 286 2604 +12
Bishop Pair Evaluation 2021 Mar 14 2960ec9 285 2592 +22
Position Cache Optimization Search 2021 Feb 23 42d7702 286 2570 +8
Move Generation Optimization Search 2021 Feb 17 22002dc 287 2562 +12
PVS and Null Move Search 2021 Feb 09 f231dac 285 2550 +20
Remove Aspiration Windows Search 2020 Dec 20 4b7963b 290 2530 +9
Time Management Search 2020 Dec 19 d143bb5 286 2521 +8
Crash Bug Search 2020 Aug 29 2d855ec 288 2513 +0
King Safety Evaluation 2020 Aug 16 6794c89 288 2513 +63
Eval Param Tuning Evaluation 2020 Jul 23 bef88d5 283 2450 +30
Late Move Pruning Search 2020 Feb 08 6f3d17a 288 2420 +29
Piece Mobility Evaluation 2020 Feb 01 5c5d4fc 282 2391 +62
Passed Pawns Evaluation 2018 Dec 27 103 279 2329 +119
Staged Move Generation Search 2018 Dec 15 93 275 2210 +39
History Heuristics Search 2018 Dec 03 84 275 2171 +28
Eval Param Tuning Evaluation 2018 Nov 24 75 272 2143 +47
Sophisticated Search
Material and Piece Location
Baseline 2018 Nov 08 58 269 2096 0
  1. GitHub commit (hash) or Subversion source code revision (integer)
  2. Win At Chess position test, 3 seconds per position
  3. Bullet chess, 2 min / game + 1 sec / move

MadChess 3.0 Beta 2960ec9 (Bishop Pair)

I improved MadChess 3.0 Beta’s evaluation function by adding middlegame and endgame evaluation parameters for bishop pair.

Tuning code indicated the bishop pair parameters immediately reduced evaluation error when examining a database consisting of approximately 54,000 Grandmaster games (both players 2600 ELO or stronger). I ran the Particle Swarm Optimization tuner on all evaluation parameters and it further reduced evaluation error.

This improved evaluation function increased the playing strength of MadChess 3.0 Beta by 22 ELO.

 

Feature Category Date Commit1 WAC2 ELO Rating3 Improvement
Bishop Pair Evaluation 2021 Mar 14 2960ec9 285 2592 +22
Position Cache Optimization Search 2021 Feb 23 42d7702 286 2570 +8
Move Generation Optimization Search 2021 Feb 17 22002dc 287 2562 +12
PVS and Null Move Search 2021 Feb 09 f231dac 285 2550 +20
Remove Aspiration Windows Search 2020 Dec 20 4b7963b 290 2530 +9
Time Management Search 2020 Dec 19 d143bb5 286 2521 +8
Crash Bug Search 2020 Aug 29 2d855ec 288 2513 +0
King Safety Evaluation 2020 Aug 16 6794c89 288 2513 +63
Eval Param Tuning Evaluation 2020 Jul 23 bef88d5 283 2450 +30
Late Move Pruning Search 2020 Feb 08 6f3d17a 288 2420 +29
Piece Mobility Evaluation 2020 Feb 01 5c5d4fc 282 2391 +62
Passed Pawns Evaluation 2018 Dec 27 103 279 2329 +119
Staged Move Generation Search 2018 Dec 15 93 275 2210 +39
History Heuristics Search 2018 Dec 03 84 275 2171 +28
Eval Param Tuning Evaluation 2018 Nov 24 75 272 2143 +47
Sophisticated Search
Material and Piece Location
Baseline 2018 Nov 08 58 269 2096 0
  1. GitHub commit (hash) or Subversion source code revision (integer)
  2. Win At Chess position test, 3 seconds per position
  3. Bullet chess, 2 min / game + 1 sec / move