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# Smallchesslib

Smallchesslib (SCL) is a small CC0 LRS/suckless chess library written in C by drummyfish. It is a single header library written in fewer than 4000 lines of code, with no dependencies and comes with a tiny engine called smolchess. It implements the basic chess rules as well as those of chess960, convenience functions (even primitive board picture export), handling of basic formats such as FEN and PGN, and a simple alpha-beta minimax AI -- smolchess played on lichess where it got a rating of about 1500 (https://lichess.org/@/smolchessbot); trying some test games against engines from CCRL, where the strongest stockfish 17 is currently rated 3642 Elo, revealed an Elo roughly around 600 (on occasion beat a 700 rated engine, consistently beat a 550 Elo one). The repository is available at https://git.coom.tech/drummyfish/smallchesslib. Smallchesslib/smolchess is extremely simple and has been tested to run even on super weak platform such as Arduboy which only has some 2 KB of RAM! That's pretty incredible, no?

         A B C D E F G H
      8  r:: b:: k:: n:r
      7 :: p:p  :: p::
      6  p:b n::  ::  :p
      5 ::# ::  :P  :p
      4   ::#B:: N::  ::
      3 :P  ::  :: N::
      2   :P P:B  :: P:P
      1 :R  ::  :K  :: R

    white played b5c4
    black to move

    ply number: 27
    FEN: r1b1k1nr/1pp2p2/pbn4p/4P1p1/2B1N3/P4N2/1PPB2PP/R3K2R b KQ - 1 14
    board static evaluation: 0.167969 (43)
    board hash: 3262264934
    phase: midgame
    en passant: 0
    50 move rule count: 1
    PGN: 1. e4 e5 2. d4 d5 3. dxe5 dxe4 4. Qxd8+ Kxd8 5. f3 exf3 6. Nxf3 Ke8 7. Nc3 Bb4 8. a3 Bc5 9. Ne4 Bb6 10. Bg5 h6 11. Bf4 g5
    12. Bb5+ Nc6 13. Bd2 a6 14. Bc4*

"Screenshot" of smolchess in action, playing against itself.

Technical details: it's no stockfish, simplicity is favored over strength. Evaluation function is hand-made with 16 bit integer score (no float!). Board is represented as an array of bytes (in fact it's an array of ASCII characters that can conveniently be printed right out). AI uses basic recursive minimax and alpha-beta pruning, with quiescence handled by extending search for exchanges and checks. For simplicity there is no transposition table, opening book or similar fanciness, so the performance isn't great, but it manages to play some intermediate moves. Xboard is supported.

While there are many high level engines focusing on maximizing playing strength, there are almost no seriously simple ones focusing on other points -- smallchesslib/smolchess tries to change this. It can be used as an educational tool, a basis for other engines, a simple engine runnable on weak devices, a helper for processing standard chess formats etc.

{ Although I think it would be possible to increase the playing strength a lot considering there are extremely simple engines that reach much higher Elo, TBH I am still a noob at chess programming and I suspect I have some kind of oversight in the algorithm, perhaps something related to the horizon effect. The AI doesn't make obvious blunders like hanging pieces so it's not easy to pinpoint what exactly should be improved, perhaps it's just tuning of the evaluation function or low number of searched nodes -- I have quickly tried to do things such as tuning of the parameters, adding opening book or transposition table, but it never seemed to detectably increase playing strength, and I also don't have a modern computer that would allow me to do hardcore bruteforce tuning, my computational resources are limit. If someone experienced discovers what's the thing to improve, I'd be very glad, thank you. ~drummyfish }

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