Okay, so I've seen a few threads about Dominion AI, which I have tried to ignore, but I don't think I can anymore.
I'm doing machine learning stuff right now, and in the past have messed around with game AIs. If you want you can read some blog posts I've written about the subject,
here and
here and
here and
here. None of those posts will be necessary, I'm linking them just to prove I have thought about this stuff before.
This is a subject where I can probably rant for a long time, and I'm not looking to rant, so my hot takes are
1. CouncilRoom has logged all games from Isotropic days, if you're interested I'm sure you could asked the current runner of CouncilRoom for the database.
2. I'm not that concerned by the number of starting states. It makes your problem harder, but is not a deal-breaker by itself. Chess / Checkers / Go only have 1 starting state, but it quickly branches into several different situations. If you take a start-of-the-art and throw it in the middle of a random Chess / Checkers / Go game, we'd expect it to perform well. A good AI should generalize to different game states - a good Dominion AI should do the same. That being said, it is definitely easier if you stick to a fixed board, but it isn't as interesting of a problem.
If anything I'm concerned more about the number of unique things cards do. Stuff like Young Witch or Landmarks or Events, where their mere existence drastically changes the landscape, and each one is super different. Game difficulty is guided by a mix of branching factor and ability to generalize across states.
3. Randomness is definitely an issue, but this doesn't have any relation to whether a Dominion bot can calculate an exact win chance. To be pedantic, it can, it's just not guaranteed to be accurate or to have low-variance estimates. But sometimes that's fine, as long as the right move is given higher value than all the wrong moves. Although this does affect search trees, I think credit assignment is a more important problem - it's a lot harder to attribute winning / losing moves when the game is random.
4. I'm not sure you need to learn all the small choices. I could see some handcoded heuristics taking you very far for that.
5. Basically I think 5 is the same as point 2.
AFAIK, the project Dan is on is focusing on Base-only. To me, that seems doable but also non-trivial. Superhuman performance in full Dominion sounds really, really hard. I think difficulty-wise, full Dominion is harder than multiplayer Texas Hold'Em, because there's so much fiddly stuff / uniqueness in all the different cards. I don't think this is a "12 ML researchers for 1 year" project. I think full Dominion is more like a 30-50 ML researchers project, and even with that many people, they only have about a 20% chance of doing it in a year.
Edit: realistically, the biggest thing stopping it is that there aren't enough people interested. The Computer Go community wasn't that big, but was around for long enough to make several very fast Go engines (~thousands of games/sec on a single CPU thread running highly optimized C) + hold Computer Go tournaments + get servers to support letting bots play against humans. The Dominion community is a lot newer and not as many people care about the problem.