I know this is probably a long shot, but I'm not sure where else to ask so I'm going to take a shot.
I've designed and abstract board game (think chess, shogi, go, etc) and have completed coding the rules for play against an AI player, however getting the actual AI to be good is a whole other problem.
I would love if someone who is experienced in ML would be interested in collaborating on this open source project.
The game is strictly a hobby project, with absolutely no plans for monitization or anything. Currently it's playable in the browser against AI (no multiplayer yet set up) at: https://greenants.github.io/Amalgam_Webgame/
GitHub Repo: https://github.com/GreenAnts/Amalgam_Webgame
Disclaimer: I've mostly used AI to code this project, as I'm a pretty novice programmer. Obviously that's controversial, so I want to make that clear - but remember this is simply a hobby project, and is a way for me to get my board game design digitized and actually played by others. The code will likely be a bit on the messy side, but I think for the most part the ML coder would only be interacting with the controller - so shouldn't be too much of a factor.
From my limited understanding, the actual search depth and complexity of the game is quite high, far higher than chess, so it's been quite hard for me to try and get this set up even with the help of AI coding with hueristics.
If you are interested in in the project at all, I'm always looking for help to farther this project - as I've been working on the board game itself (on and off) for more than 10 years.
The GitHub Repo listed above (in the README.md) has a graphical rulebook as well as a video tutorial linked for you to learn the rules and get an idea of the game complexity if you are interested.
Like I said, I know this is a long shot, and unlikely anyone will be interested, but I figured I'd give it a shot :)
Hi. The game doesn't work for me on mobile sadly so I can't offer any specific suggestions but in general I suggest you have two options...
The basic approach is to write some rules/ heuristics for your AI player (e.g. start with move X, if player does X then do Y, if condition X then do Y). You can probably distil some basics from your own strategy.
An advanced approach is to train a model using e.g. reinforcement learning to figure out it's own policy of which actions to take given the game state. You can use a library like stable baselines for this. You may be lucky though to train your agents by pitching them against one another or you might have to teach them e.g. with the rules-based agent or by having them learn from human-vs-human games.
Yeah (it's not extremely optimized for mobile yet, sorry).
As for the suggestions, thanks - I'll definitely look into that library as well :)