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an [[UCI]] compatible [[Chess#Variants|chess variant]] [[:Category:Open Source|open source]] engine licensed under the [[Free Software Foundation#GPL|GPL v3.0]].
CrazyAra started as a [https://en.wikipedia.org/wiki/Academic_term#Synonyms semester] project by [[Johannes Czech]], [[Moritz Willig]] and [[Alena Beyer]] for the course ''Deep Learning: Methods and Architectures'' at the [[Darmstadt University of Technology|TU Darmstadt]] in summer 2018, headed by [[Kristian Kersting]] and [[Johannes Fürnkranz]].
The project was inspired by the [[Deep Learning|deep learning]] and [[Monte-Carlo Tree Search|MCTS]] techniques described in [[DeepMind|DeepMind's]] the [[AlphaZero]] papers
<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>
<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]] ('''2018'''). ''[http://science.sciencemag.org/content/362/6419/1140 A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play]''. [https://en.wikipedia.org/wiki/Science_(journal) Science], Vol. 362, No. 6419</ref>,
