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Abstract
The idea of training Articial Neural Networks to evaluate chess positions has been widely explored in the last ten years. In this paper we investigated dataset impact on chess position evaluation. We created two datasets with over 1.6 million unique chess positions each. In one of those we also included randomly generated positions resulting from consideration of potentially unpredictable chess moves. Each position was evaluated by the Stockfish engine. Afterwards, we created a multi class evaluation model using Multilayer Perceptron. Solution to the evaluation problem was tested with three different data labeling methods and three different board representations. We show that the accuracy for the model trained for the dataset without randomly generated positions is higher than for the model with such positions, for all data representations and 3, 5 and 11 evaluation classes.
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Full text
- Publication version
- Accepted or Published Version
- License
- Copyright (2023 The Author(s), under exclusive license to Springer Nature Switzerland AG)
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- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language:
- English
- Publication year:
- 2023
- Bibliographic description:
- Wieczerzak D., Czarnul P.: Dataset Related Experimental Investigation of Chess Position Evaluation Using a Deep Neural Network// / : , 2023,
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-031-30442-2_32
- Sources of funding:
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- Statutory activity/subsidy
- Verified by:
- Gdańsk University of Technology
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