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The article describes testing a weakness of the go-playing AI KataGo (9d) detected by K. Pelrine et al. Building groups following the developed 4-level algorithm reveals the incapability of AI to understand intentional sacrifice of larger groups and to prioritize when discovering several similar sacrifices on the board. The value of the developed strategy lies in human victory over a potentially unbeatable program even regarding the fact that human is playing on the level of 1q (2000 Elo).
go game, baduk, weiqi, KataGo, neural networks
1. AlphaGo by Google DeepMind. URL: https://www.deepmind.com/research/highlighted-research/alphago (accessed 20.09.2023).
2. KataGo on GitHub. URL: https://github.com/lightvector/KataGo/releases?q=v1.12.4&expanded=true (accessed 20.09.2023).
3. Leela official website. URL: https://www.sjeng.org/leela.html (accessed 20.09.2023).
4. Lauder E. Tencent's Made a Go-Playing AI to Rival Google's AlphaGo. URL: https://aibusiness.com/companies/tencent-s-made-a-go-playing-ai-to-rival-google-s-alphago (accessed 20.09.2023).
5. Wang T.T., Gleave A., Tseng T., Pelrine K., Belrose N. et al. Adversarial policies beat superhuman Go AIs // arXiv:2211.00241 [cs.LG]. URL: https://goattack.far.ai/adversarial-policy-katago (accessed 20.09.2023). DOI:https://doi.org/10.48550/arXiv.2211.00241
6. Financial Times. Man beats machine at Go in human victory over AI. URL: https://www.ft.com/content/175e5314-a7f7-4741-a786-273219f433a1 (accessed 27.11.2023).