Evaluating knowledge transferability in chess endgames using deep neural networks

Transfer learning is becoming an essential part of modern machine learning, especially in the field of deep neural networks. In the domain of image recognition there are known methods to evaluate the transferability of features which are based on evaluating to what degree a feature extractor can be considered general to the domain, or specific to the task at hand. This is of high importance when aiming for a successful knowledge transfer since one typically wants to transfer only the general feature extractors and leave the specific ones behind. The general features in the case of image classification can be considered local with respect to each pixel, since the feature extractors in early layers activate on simple features like edges, which are localized within a certain radius from a given pixel. One might then ask the question, whether similar methods are also applicable in other domains than image classification, and of special interest are domains characterized by non-local features. Chess is as excellent example of such a domain since a square's locality can not be defined by the adjacent pixels alone. One needs to take into account that a single piece can traverse the whole board in a single move. We show that this method is applicable in the case of chess endgame tablebases, in spite of structural differences in the feature space, and that the distribution of the learned information within the network is similar as in the case of image classification.

Author dc:creator

• Frímann Freyr Kjerúlf Björnsson 1978-

Contributors dc:contributor

• Háskólinn í Reykjavík

• Tölvunarfræði
• Meistaraprófsritgerðir
• Skák
• Vélrænt nám
• Gervigreind
• Computer science
• Machine learning
• Artificial intelligence
• Chess

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/1946/33580

OAI identifier oai:identifier

oai:skemman.is:1946/33580