Learning to Evaluate Chess Positions with Deep Neural Networks and Limited Lookahead

Sabatelli, Matthia; Bidoia, Francesco; Codreanu, Valeriu; Wiering, Marco A.

doi:10.5220/0006535502760283

Cited by 7 publications

(6 citation statements)

References 8 publications

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“…Te optimal combination of hidden layer sizes was found using the validation subset, by performing an exhaustive trial and error procedure in the range [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] and [5][6][7][8][9][10] for the frst and the second layer, respectively. Te other rival method concerns a CNN model of two 2D convolutional layers with 20 5 × 5 and 50 3 × 3 flters in the frst and the second layer, respectively, trained with stochastic gradient descent (SGD) algorithm, as proposed in [4]. Due to their stochastic nature, the MLP and CNN models produce a diferent result for each run, and thus, 30 diferent runs were performed in order to test the consistency of the results.…”

Section: Case Studymentioning

confidence: 99%

“…Nowadays, even top-level grandmasters use chess engines for their tournament preparation and overall training. Advancements in the felds of artifcial intelligence and machine learning have led to new ideas about engines, like chessplaying agents with no coding of any chess rules in them whatsoever and enhancements on the position evaluation algorithms with the aid of neural networks replacing, for example, the heuristic function used in a tree search algorithm [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…In [14], various machine learning approaches such as regression and Naive Bayes classifers are used for predicting chess game results. In [4], MLP and CNNs of predetermined sizes are tested in various chess modeling approaches, as for example chess position evaluations and moves classifcation; the experimental results showed the superiority of MLP models, despite chessboard representation methods, such as bitmap and algebraic input. In [15], a deep neural network paired with a look-ahead algorithm for evaluation function is presented.…”

Section: Introductionmentioning

confidence: 99%

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Chess Position Evaluation Using Radial Basis Function Neural Networks

2023

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The game of chess is the most widely examined game in the field of artificial intelligence and machine learning. In this work, we propose a new method for obtaining the evaluation of a chess position without using tree search and examining each candidate move separately, like a chess engine does. Instead of exploring the search tree in order to look several moves ahead, we propose to use the much faster and less computationally demanding estimations of a properly trained neural network. Such an approach offers the benefit of having an estimation for the position evaluation in a matter of milliseconds, while the time needed by a chess engine may be several orders of magnitude longer. The proposed approach introduces models based on the radial basis function (RBF) neural network architecture trained with the fuzzy means algorithm, in conjunction with a novel set of input features; different methods of network training are also examined and compared, involving the multilayer perceptron (MLP) and convolutional neural network (CNN) architectures and a different set of input features. All methods were based upon a new dataset, which was developed in the context of this work, derived by a collection of over 1500 top-level chess games. A Java application was developed for processing the games and extracting certain features from the arising positions in order to construct the dataset, which contained data from over 80,000 positions. Various networks were trained and tested as we considered different variations of each method regarding input variable configurations and dataset filtering. Ultimately, the results indicated that the proposed approach was the best in performance. The models produced with the proposed approach are suitable for integration in model-based decision-making frameworks, e.g., model predictive control (MPC) schemes, which could form the basis for a fully-fledged chess-playing software.

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Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Chess Position Evaluation Using Radial Basis Function Neural Networks

2023

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“…While computer chess has been explored extensively, there are very few papers that specifically focus on playing the game with limited look ahead. The most notable work that addresses this is by Sabatelli et al [2018], which is the primary influence of this research. In this work, a supervised learning approach was used to train an artificial neural network that served as a chess board evaluator.…”

Section: Prior Workmentioning

confidence: 99%

“…Similar to the dataset proposed by Sabatelli et al [2018], we extract unique board configurations from public chess databases. The board configurations are labelled using Stockfish 11, where each label represents the centipawn advantage relative to white.…”

Section: The Datasetmentioning

confidence: 99%

Playing Chess with Limited Look Ahead

Maesumi

2020

Preprint

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We have seen numerous machine learning methods tackle the game of chess over the years. However, one common element in these works is the necessity of a finely optimized look ahead algorithm. The particular interest of this research lies with creating a chess engine that is highly capable, but restricted in its look ahead depth. We train a deep neural network to serve as a static evaluation function, which is accompanied by a relatively simple look ahead algorithm. We show that our static evaluation function has encoded some semblance of look ahead knowledge, and is comparable to classical evaluation functions. The strength of our chess engine is assessed by comparing its proposed moves against those proposed by Stockfish. We show that, despite strict restrictions on look ahead depth, our engine recommends moves of equal strength in roughly 83% of our sample positions.Preprint. Under review.

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Dataset Related Experimental Investigation of Chess Position Evaluation Using a Deep Neural Network

Wieczerzak

Czarnul

2023

Parallel Processing and Applied Mathematics

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Learning to Evaluate Chess Positions with Deep Neural Networks and Limited Lookahead

Cited by 7 publications

References 8 publications

Chess Position Evaluation Using Radial Basis Function Neural Networks

Chess Position Evaluation Using Radial Basis Function Neural Networks

Playing Chess with Limited Look Ahead

Dataset Related Experimental Investigation of Chess Position Evaluation Using a Deep Neural Network

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