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In his M.Sc thesis, supervised by [[Marco Wiering]] and [[Valeriu Codreanu]], Matthia Sabatelli elaborates on [[Learning|learning]] to play chess with minimal [[Search|lookahead]], using [[Neural Networks#Deep|multilayer perceptrons]] versus [[Neural Networks#Convolutional|convolutional neural networks]] to approximate [[Stockfish|Stockfish’s]] [[Evaluation|evaluation]], also comparing two different [[Board Representation|board representations]] for the input layer

<ref>[[Matthia Sabatelli]] ('''2017'''). ''Learning to Play Chess with Minimal Lookahead and Deep Value Neural Networks''. Master's thesis, [https://en.wikipedia.org/wiki/University_of_Groningen University of Groningen], [https://www.ai.rug.nl/~mwiering/Thesis_Matthia_Sabatelli.pdf pdf]</ref>.

=Selected Publications=