On the Value of Chess Squares

Area of Science:

• Artificial Intelligence
• Computational Game Theory
• Chess Analytics

Background:

• Traditional chess engines assign static, piece-centric values.
• Assessing piece-square interactions is crucial for accurate positional evaluation.
• Existing methods lack dynamic valuation of piece placement.

Purpose of the Study:

• To develop a neural network model for dynamic chess square-piece valuation.
• To improve upon conventional fixed piece value systems.
• To quantify the advantage or disadvantage of specific piece placements.

Main Methods:

• Utilizing a neural network architecture.
• Inputting a triplet (color, piece, square) for analysis.
• Employing deep Q-learning to estimate model parameters.
• Introducing marginal valuations for enhanced accuracy.

Main Results:

• The model accurately assesses the worth of chess positions.
• Demonstrated effectiveness in evaluating knight and bishop positioning.
• Provided insights into pawn valuation based on square-piece combinations.

Conclusions:

• Neural network-based marginal valuations offer a superior approach to traditional chess piece values.
• The model provides a dynamic and context-aware evaluation of chess positions.
• Future research can expand this methodology to other chess elements and strategies.