Can we look forward to a Netrunner card generator? :)
GAN-generated art, name and flavor text, a perhaps more structured rules engine for the card type and behavior, and AlphaZero to play the card in a deck against itself a few thousand times to tune the balance.
Man, I was really hoping to be the first to pull the trigger on that. But if I'm being honest with myself: I ain't gonna be putting in the time to learn to tensorflow.
Tensorflow (or PyTorch, or any other framework) aren't the problem, though.
It's choosing a suitable net architecture, loss function, and training methodology all of which require in-depth knowledge and lots of experimentation.
Even after that you're only half way there, because now the fun and exploration is over and the pain begins: finding (and worse even - annotating!) and validating training data.
Once this herculean task has been finished, it's time to get your credit card ready and shell out hundreds of dollars/pounds/euros for days of GPU time to train the basic model.
With that out of the way, it's time for a little fun to return and finetune your model(s), which usually can be done using even a mediocre desktop PC or laptop.
There's a reason you can make serious money with even a comparatively simple and straight forward idea that uses ML and AI to get quality results. It's much, much more involved than just "learning some tensorflow".
> It's choosing a suitable net architecture, loss function, and training methodology all of which require in-depth knowledge and lots of experimentation.
Yes, things have progressed since my time in undergrad studying NNs and such. Presumably the work done on MtG transfers.
> Even after that you're only half way there, because now the fun and exploration is over and the pain begins: finding (and worse even - annotating!) and validating training data.
Well, for A:NR there's already multiple databases and a thousand cards. That's your training set. Throw in the online league cards if you require more.
> It's much, much more involved "learning some tensorflow".
I already work in SRE for a huge AI you've heard of, but thanks for the lecture.
A thousand cards is not much of a training set so I'm not really sure deep learnign would get very far.
A few years ago there was a community project training RNNs to generate M:tG cards [1], that if I'm not mistaken became Roborosewater [2]. M:tG already had around 12-15k cards by the time and still the majority of generated cards did not make sense. In the mtgsalvation thread in [1], most of the time people crack out with the nonsense that's generated by the RNNs people train [3]. There are also plenty of cards that make sense and are interesting and even usable, but they had to be hand-picked out of buckets of nonsense. A lot of curation and probably editing generated cards by hand would be needed, which somewhat defeats the purpose of the whole endeavour.
To generate new cards it would make much more sense to use good old procedural generation starting with a hand-crafted grammar of the rules text. This is certainly doable for a game of the size of M:tG, let alone Netrunner. For instance, M:tG Arena, the online p2p version of the game, runs on a game engine with a hand-crafted parser, the Game Rules Parser, that essentially resolves spells like an interpreter executing a script [4][5].
I mean to say, sometimes jumping feet-first into learning how to use a new set of complex tools is not necessary. Simpler tools that should already be in every programmer's toolbox can do the job fine, sometimes, even when it seems easier to just get a bunch of data and put it through a machine learning meat grinder. "Easier" might turn out to mean you need to do a lot more work before and after you can use the "easier" method (e.g. labelling, training, curation, etc) and not even get very good results in the end.
[5] To tout my own horn a bit, I did the same thing in my degree as a final year project, but I didn't have enough time to get full coverage of the entire card corpus at the time - still, I was just one undergrad student and I did manage to get a big chunk of the game working with a rules parser. The parser was written in Prolog meaning I could "run it backwards" as a generator so the project had an M:tG ability text generator that spat out grammatically correct, if not always particularly useful, text. I'm not linking to the project because it was 9 years ago and it's painfully embarrassing looking at it now.
GAN-generated art, name and flavor text, a perhaps more structured rules engine for the card type and behavior, and AlphaZero to play the card in a deck against itself a few thousand times to tune the balance.