[grraaaaahhh]

I think you'd have to go even narrower by predefined the AI's opponent's deck as well before you'd get an AI that consistently outperformed humans. Even the most all in combo or burn deck will drop lose percentage points if they don't take into account what they're playing against and what hate cards to play around.

[klmadfejno]

By predefined, I meant comprised from a pool of cards that the AI is familiar with. But now that I think about it, it is a much more interesting problem. Most of the big name bots, like the dota one, do have some model for picking a character. But the scope of choices is very limited to the point where even if it's "one model" it can have a large space dedicated to specifically that character. Would current techniques be sufficient to, given enough training data on each individual card, be able to compose a deck to fit arbitrarily assigned constraints and find a way to play it effectively? I don't know, but the answer sounds a lot closer to the idea of "strategy" that researchers are trying get at but missing the mark on in Starcraft and what not.

I have no idea how they'd handle that. It's a great way to prevent the bot from learning a single dominant strategy and trying to just execute it perfectly.