Self Annotation - how AI can help

By GilaChess - March 08, 2024


After the hard game, the chess player retires to his hotel room. Takes an hour rest. Then takes up the scoresheet and sets up the game on an actual chessboard - not on the computer screen or phone. With a paper and the game is slowly annotated with evaluations, variations and lines that may or may not have been better etc. Finishing, a picture is take and sent to an AI program that then evaluates the hand written annotation.

A detailed reply is then sent back to the chess player where there is eager anticipation in how right or wrong the annotation sent was. This becomes a routine with the further rounds of the tournament.

This sounds close to science fiction doesn't it. But like my last article about AI Coach, I  believe it is not in our distant future but coming soon. This is probably what a human coach may do with students but LLMs can also duplicate this function.

  1. Manual Annotations and Evaluations:

    • The chess player annotates their game by hand, using notations like “±” (indicating that White is better) or “=” (indicating equality).
    • These annotations capture the player’s understanding of the game state, strategic choices, and positional assessments.
  2. LLM Evaluation:

    • The LLM analyzes the player’s annotations and evaluates their accuracy.
    • It verifies whether the player’s assessments align with established chess principles and objective evaluations.
    • The LLM can provide a quantitative measure of how well the player’s annotations match the actual game dynamics.
  3. Benefits of this Feedback Approach:

    • Learning Reinforcement: By receiving feedback on their annotations, the player reinforces their understanding of chess concepts. Correct annotations are reinforced, while incorrect ones are corrected.
    • Identifying Mistakes: The LLM highlights where the player’s evaluations deviate from optimal play. This pinpointing helps players recognize their mistakes and learn from them.
    • Strategic Insights: Detailed feedback allows players to understand why certain moves were better or worse. It provides strategic insights beyond mere correctness.
    • Pattern Recognition: Consistent feedback helps players recognize recurring patterns and positional themes.
    • Self-Reflection: Players can reflect on their thought process during the game and identify areas for improvement.
  4. Challenges and Considerations:

    • Subjectivity: Chess evaluations can be subjective. Different annotators may have varying opinions on a position’s quality.
    • Complexity: Chess is multifaceted, involving tactics, strategy, and positional understanding. The LLM must consider all aspects.
    • Human Bias: The LLM should account for biases inherent in human annotations.
    • Feedback Granularity: Balancing detailed feedback with simplicity is crucial. Too much detail may overwhelm the player.
  5. Enhancing Chess Learning:

    • When used judiciously, this feedback approach can significantly enhance chess learning.
    • Players gain insights into their decision-making process, leading to better play over time.
    • The LLM acts as a personalized coach, guiding players toward more accurate assessments and deeper understanding.

In summary, while challenges exist, a well-designed chess LLM that evaluates manual annotations can be a valuable tool for players seeking to improve their game. It complements traditional study methods and encourages thoughtful analysis. 

Also, the LLM will recognize common 'ghosts' or imaginary fears of the player as more games annotation are examined. These can be highlighted and a remedy or cure can be prepared.

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