I want to be able to calculate how much points each player has for a board game by taking a picture on the board game. I do this as hobby, not for university or professional purposes. I will use it while playing with my friends and also I hope this will help me build some knowledge about ML.
I plan to do that with supervised learning. Take many pictures of the game and tell the machine what is the correct output. The game scoring is not something straight-forward. Players may control different territories. A territory is controller if a player has units there even if there is enemy buildings. The game is Scythe. From my previous knowledge about ML I know that small changes in the image (for example the angle) from the images you gave to the machine to learn may bug it to recognise them.
Questions (I do not want someone to tell me how to do it but rather which approach is better so I can read more about it):
- Is supervised learning the best option in this case?
- I plan to build many micro neural-networks for the different cases - recognise who controls a territory and which territory (which territory seems very hard); separate network to recognise if there are resources on some players territory (this seems very hard, as the combination of resources might be from 0 to many; shall I just put different combinations of resources (+numbers of them) and take many pictures?). There are lots of combinations. I cannot take a pic on every of them.