Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Training a Convolutional Neural Network to Evaluate Chess Positions
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Användning av ett konvolutionellt neuronnät för att evaluera schackpositioner (Swedish)
Abstract [en]

Convolutional neural networks are typically applied to image analysis problems. We investigate whether a simple convolutional neural network can be trained to evaluate chess positions by means of predicting Stockfish (an existing chess engine) evaluations. Publicly available data from lichess.org was used, and we obtained a final MSE of 863.48 and MAE of 12.18 on our test dataset (with labels ranging from -255 to +255). To accomplish better results, we conclude that a more capable model architecture must be used.

Abstract [sv]

Konvolutionella neuronnät används ofta för bildanalys. Vi undersöker om ett enkelt sådant nätverk kan tränas att evaluera schackpositioner genom att förutspå värderingar från Stockfish (en existerande schackdator). Vi använde offentligt tillgänglig data från lichess.org, och erhöll en slutgiltig MSE 863.48 och MAE 12.18 på vår testdata (med data i intervallet -255 till +255). För att uppnå bättre resultat drar vi slutsatsen att en mer kapabel modellarkitektur måste användas.

Place, publisher, year, edition, pages
2019. , p. 18
Series
TRITA-EECS-EX ; 2019:377
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-263062OAI: oai:DiVA.org:kth-263062DiVA, id: diva2:1366229
Supervisors
Examiners
Available from: 2019-11-12 Created: 2019-10-28 Last updated: 2019-11-12Bibliographically approved

Open Access in DiVA

fulltext(1093 kB)9 downloads
File information
File name FULLTEXT01.pdfFile size 1093 kBChecksum SHA-512
318762ec212a463b7d590ebfb2ce7af352b70e1655e12a43d6d95fe2a0363b146e096289cea636ed1f8d913d40c587060d84c6dfecf03349c0503916748141e0
Type fulltextMimetype application/pdf

By organisation
School of Electrical Engineering and Computer Science (EECS)
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 9 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 33 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf