State of Chess AI for Move Interpretation
Chess, a game with origins traced back to 500 A.D., has undergone numerous variations and rule changes as it spread across the globe. Its early form, chaturanga, emerged in Asia and evolved significantly over the centuries. By the late 19th century, a standardized format led to the development of chess theory. The game saw different strategic eras, such as daring attacks in the 1800s, positional chess until the 1920s, and the hypermodernism approach. The Soviet Union dominated world championships from 1927 to 2006, coinciding with the rise of chess computers. Garry Kasparov's reign as world champion and his eventual defeat by IBM's Deep Blue in 1997 marked a significant milestone in the influence of artificial intelligence on chess.
Chess computers like Deep Blue and Hydra calculate moves using AI techniques, treating chess as a Tree Search problem. They evaluate board positions and possible moves to maximize their advantage and minimize the opponent's chances of winning. The Minimax algorithm, coupled with an evaluation function, helps these engines decide the best moves by assessing the game's potential outcomes. Despite the vast number of possible chess games (estimated at 10^120), advancements in AI, such as deep learning networks and Convolutional Neural Networks, have improved the efficiency and effectiveness of these engines, enabling them to outperform human players consistently.
Chess engines determine the best moves using advanced algorithms and evaluation functions. They treat chess as a tree search problem, employing the Minimax algorithm to maximize their own score while minimizing the opponent's potential score. The evaluation function assigns numerical values to board states, considering factors like material count, control of the center, and king safety. To optimize the search process, engines use alpha-beta pruning to eliminate obviously worse moves and heuristics to focus on promising lines of play. Modern engines also leverage deep learning networks to recognize patterns and evaluate positions more accurately. Through iterative deepening, engines search to increasing depths, selecting the move with the highest evaluation score. Continuous learning from past games further refines their performance, enabling them to surpass human strategic depth and accuracy.
Learning from Chess Engines
The next question is: how do we learn from a chess engine? Is it possible to use something like OpenAI, ChatGPT, Gemini, or another AI to provide human-readable interpretations for why the engine made a move? Some sites, such as DecodeChess, claim they do, but do they really? For example, DecodeChess attempted to describe why Kxc6 is a blunder, stating it allows the queen on c7 to guard g3. However, the queen is actually on d8, and g3 is an empty square, making the explanation confusing and unhelpful. It might have been more useful to mention that the knight can be taken by the bishop. Despite a great UI, the AI behind such explanations often fails at conceptualizing the moves and their reasons.
Another example involves using Gemini to analyze a game:
[Event "Live Chess"][Site "Chess.com"][Date "2024.06.05"][Round "?"][White "sbernst8"][Black "Thejjm"][Result "0-1"][ECO "A10"][WhiteElo "1266"][BlackElo "1294"][TimeControl "900+10"][EndTime "7:06:51 PDT"][Termination "Thejjm won by resignation"]1. c4 d5 2. cxd5 Qxd5 3. Nc3 Qd8 4. e4 e5 5. Nf3 Nc6 6. Bb5 f6 7. O-O Bd7 8. d3
Bb4 9. Bd2 a6 10. Bxc6 Bxc6 11. a3 Bxc3 12. bxc3 Ne7 13. Qb3 Qxd3 14. Rfe1 Bxe415. Rad1 Bxf3 16. Re3 Bxd1 17. Rf3 0-1
When I asked Gemini to analyze this game, it claimed that Rfe1 was a critical mistake by weakening the e-file. However, Stockfish identified Rfe1 as the best move. This discrepancy highlights the limitations of current AI in providing accurate move interpretations.
For those seeking a more reliable chess move analyzer or chess trainer, KnightlyChess offers a promising alternative. KnightlyChess provides an engine calculation of move accuracy across the opening, middlegame, endgame, and overall, comparing it against a database of 1 million chess games. This comprehensive analysis helps players understand their performance at different stages of the game. Additionally, future plans for KnightlyChess include generating tactics puzzles from a user's worst 20 moves over their last 20 games, offering targeted practice to improve their skills. By integrating sophisticated algorithms with user-friendly interfaces, KnightlyChess enhances the learning experience and aids in the development of better chess skills.
Until a major chess engine like Stockfish develops an AI that reliably explains move choices, using general AI programs like ChatGPT, OpenAI, or Gemini for learning purposes remains insufficient. Current tools do not consistently offer the conceptual clarity necessary for effective learning and improvement in chess.
