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Thanks for this! I'm currently designing a language for complex board games like Battlestar Galactica: http://www.adama-lang.org/
Something that I found amazing was inverting the flow control such that the server asks players questions with a list of possible choices simplifies the agent design tremendously. As I'm looking to retire to work on this project, I can generate the agent code and then hand-craft an AI. However, some AIs are soooo hard to even conceptualize.
If you are interested in this, I maintain a list of boardgame-solving related research at https://github.com/captn3m0/boardgame-research, with sections for specific games.
This looks really interesting. It would be a good project to test this against a general card-playing framework to easily test it on a variety of imperfect-information games based on playing cards.
You can also just download pre-trained models. Get those set up and then install Sabaki (https://sabaki.yichuanshen.de/) and connect it to your KataGo... instant (ok, a few hours probably if it's your first time setting it up) superhuman Go AI. There's even an npm package you can use to process SGF files and automatically score moves as good/questionable/bad + generate variations that were better choices: https://github.com/9beach/analyze-sgf/blob/master/README.en-...
You can also just download pre-trained models. Get those set up and then install Sabaki (https://sabaki.yichuanshen.de/) and connect it to your KataGo... instant (ok, a few hours probably if it's your first time setting it up) superhuman Go AI. There's even an npm package you can use to process SGF files and automatically score moves as good/questionable/bad + generate variations that were better choices: https://github.com/9beach/analyze-sgf/blob/master/README.en-...