The Value of Chess Squares
This work addresses the need for more nuanced chess position evaluation for players and AI developers, but it is incremental as it builds on existing chess AI methods without introducing a new paradigm.
The authors tackled the problem of assigning dynamic values to chess piece-square combinations by proposing a neural network-based approach using deep Q-learning, which enhances traditional fixed piece valuations by introducing marginal valuations for more accurate position assessment.
We propose a neural network-based approach to calculate the value of a chess square-piece combination. Our model takes a triplet (Color, Piece, Square) as an input and calculates a value that measures the advantage/disadvantage of having this piece on this square. Our methods build on recent advances in chess AI, and can accurately assess the worth of positions in a game of chess. The conventional approach assigns fixed values to pieces $(\symking=\infty, \symqueen=9, \symrook=5, \symbishop=3, \symknight=3, \sympawn=1)$. We enhance this analysis by introducing marginal valuations. We use deep Q-learning to estimate the parameters of our model. We demonstrate our method by examining the positioning of Knights and Bishops, and also provide valuable insights into the valuation of pawns. Finally, we conclude by suggesting potential avenues for future research.