Toribash is definitely not as complex as Dota, but it has so many possible combinations for moves that machine learning is pretty much the only solution. Since replays are not recorded (and it would literally take years to gather enough replays even with a script), self-learning seems to be the best option. This would be much easier if there was a built-in AI (not machine learning) to play against at the start, but not having one would just mean playing more games against itself.

I'm pretty positive that such an AI is in reach without having an insane amount of resources as long as Toribash has enough information in their an API (as ynvaser said). I think a lot of you guys are not considering how quickly a Toribash AI could train itself. Sure, there are a TON of possibilities in this game but even for players a typical ABD game takes like 5 minutes, and an AI wouldn't need the reaction time—it would come up with a move in less than a second. The only information you need is the state of all 21 joints for each player for each frame. That's not intensive at all and can easily be handled by a decent computer.

I would think of a Toribash AI as being essentially the same as chess, except every piece can move every turn. Moving at the same time vs. taking turns actually doesn't make a difference at all since they're both turn-based, either way you're just trying to predict what your opponent will do next. You would almost certainly use a Minimax algorithm as well. With how much machine learning has progressed in the recent years and the libraries available to the public, I don't think this would be nearly as hard as you guys think it is.