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Allie is inspired by the seminal [[AlphaZero]] paper <ref>[[David Silver]], [[Thomas Hubert]], [[Julian Schrittwieser]], [[Ioannis Antonoglou]], [[Matthew Lai]], [[Arthur Guez]], [[Marc Lanctot]], [[Laurent Sifre]], [[Dharshan Kumaran]], [[Thore Graepel]], [[Timothy Lillicrap]], [[Karen Simonyan]], [[Demis Hassabis]] ('''2017'''). ''Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm''. [https://arxiv.org/abs/1712.01815 arXiv:1712.01815]</ref> and the [[Leela Chess Zero]] project - in particular it utilizes probes the same [[Neural Networks#Deep|deep]] [[Neural Networks#Convolutional|convolutional neural networks]] produced by Leela Chess, and replaces [[Leela Chess Zero#Lc0|Lc0]] with an own implementation of a [[UCT#PUCT|PUCT]] [[Monte-Carlo Tree Search|Monte-Carlo tree search]] <ref>[https://github.com/manyoso/allie/blob/master/lib/node.h allie/node.h at master · manyoso/allie · GitHub]</ref>.