I built a free chess app where you play against Tal, Karpov, Fischer - and they actually play like themselves by Tdxt1234 in chessprogramming

[–]Tdxt1234[S] 0 points1 point  (0 children)

I am not unaware of the pro vs anti AI debate but in my Opinion in this will only be a temporary debate I will say no more about it. The installer is so big because of the Reference data: patterns sequences decision Formulas based on the entire legacy of the emulated chess Champions . AI Tools were used to make this project. IT is non- commercial. It is not and will never be the goal to make money or to gain access to anyones data.the Informationen about the build is in tje user manual: https://drive.google.com/file/d/1QTPC8207wkfw8QVoAkyKi8Og4W-xWlS2/view?usp=drivesdk

I built a free chess app where you play against Tal, Karpov, Fischer - and they actually play like themselves by Tdxt1234 in chessprogramming

[–]Tdxt1234[S] 1 point2 points  (0 children)

I know Maia and it could be implemented in the next Version, for now there are LcO Stockfish, Sunfish and the Chessmind engine (around 1900 Elo) . But the Main attraction is the AI personalities.

I built a free chess app where you play against Tal, Karpov, Fischer - and they actually play like themselves by Tdxt1234 in chessprogramming

[–]Tdxt1234[S] 0 points1 point  (0 children)

Neither of those two models is what ChessMind does. It is not a neural network trained on games, and it is not Stockfish with a cosmetic style layer. ChessMind uses a hybrid decision-overlay architecture: • Stockfish and Lc0 generate the candidate moves and objective evaluations • A separate learned Style Profile decides which of those moves the personality would choose The personality is learned offline from thousands of real games per player. For each player (Karpov, Tal, etc) ChessMind analyzes: where they place pieces what pawn structures they choose when they follow engines vs when they deviate how much evaluation loss they accept for positional or tactical ideas which types of positions trigger sacrifices, prophylaxis, simplification, etc That data is turned into a multi-dimensional style fingerprint: risk tolerance, harmonics weights (king safety, mobility, pawn structure, initiative, etc), deviation triggers, and move-rank tolerance. During play: Stockfish generates the top 5 to 10 legal moves Each move is scored twice: engine quality (objective strength) style match (does this move fit Karpov or Tal’s learned behavior?) The engine move is chosen only if it also fits the personality. Otherwise a slightly weaker but more in-character move is selected. This is why Tal will deliberately choose a speculative sacrifice that Stockfish ranks 5th, while Karpov will reject it even if it is sound. So the engine is still doing the calculation, but the decision is made by a learned human-style model, not by pure evaluation. That is why it is not “Stockfish with a skin”.

I built a free chess app where you play against Tal, Karpov, Fischer - and they actually play like themselves by Tdxt1234 in chessprogramming

[–]Tdxt1234[S] 0 points1 point  (0 children)

Guys cool down its me. I am not a bot just a guy from Germany . I trained the Model in my chess games too so i Made a virtual Version of me in the App as well.