I'm with the other guy; I don't see how a person could make the AI play any better.
That's not what MCTS means.
MCTS refers to the bandit problem which formulated the search parameters. MCTS always chooses the "most interesting" path to explore. (Where "most interesting" is the path that balances explore-and-exploit hyper-parameters).
AlphaZero improved upon MCTS by deferring to the neural net as the hyper-parameter. But AlphaZero, for a given network on a given board-state, will ALWAYS choose the same position as "most interesting".
Turning AlphaZero from its current deterministic form into a random form would be an easy fix. But its just one example of how AlphaZero really isn't designed for competitive use yet (despite playing the best game of Go of all time). Instead of picking the top #1 move, maybe you randomly pick from the top 3 moves... or some other scheme.
The AI selects a move. What state is the board in now? It doesn't know, because the opponent also selected a move.
MCTS models this with a probability distribution of the states, and samples from this distribution repeatedly to build an estimate of the effectiveness of each move it could make.
But what's the probability of each move made by the opponent? And after the simulation has looked as many moves ahead as it can in the time constraints, how good a position is it in?
These are the same question, really - what's the chance of winning from this board state. In Chess you can use a heuristic algorithm to figure it out. In Go, you can't. But you can use a neural network to learn an approximation that improves as it sees more games complete.
AlphaGo does this. MCTS is a random sampling technique, and the neural net informs its probability distributions, but doesn't make it deterministic.
If given White in chess, LeelaZero plays 1. e4. Each time, every time. Guess what that means?
If you're building an opening chess database vs LeelaZero (or at least, this version of LeelaZero: https://lichess.org/@/LeelaZero-UK), you only have to worry about 1. e4 openings.
Nothing.
Play AlphaGo against itself. Go rarely has draws (requires Triple-Ko, a very, very rare position).
Almost every game you play with AlphaZero vs AlphaZero will result in a winner-and-loser. You will quickly be able to characterize the positions that AlphaZero loses in.
The strongest early moves create the most potential for winning (maximizing potential winning paths, sort of); they do not push the game towards one best end state. They do not have counters. I saw elsewhere you have some understanding of the game (15kyu) so you should be able to demonstrate this to yourself by playing some of AlphaGo’s openings on a board and trying to write deterministic counters to them. You will not be able to push the AI into a situation where it has too few options to avoid loss. You will also find you need to create a book much larger than a few moves to meaningfully predict play and so will exceed the number of states that can be stored (referencing your 16TB comment elsewhere.)
Please actually try this as I think it is a key to improving your skill in addition to understanding the challenges in automating play.
For most of us, that means we'll all have the best verison of LeelaZero to grab from Github and use in our own personal studies. Which should still be super-human in terms of play.
And don't forget that AlphaZero is gonna be getting better over that year too; you're trying to beat a moving target.
AlphaZero doesn't have any randomness built into it, does it? Which means I can build preparation against the lines of play AlphaZero would want to go down.
I think you're underestimating the human, and also overestimating Google's laboratory experiment here. No one has really played "Cyborg Go" yet.
The first casualty of "Cyborg Go" will be AlphaGo in its current form: its clearly inadequate for AlphaGo to play deterministic. Random play MUST be incorporated, lest opponent's preparation sends it down the wrong path.
If I know that AlphaGo plays slightly worse on 3-4 opening (or 4-4opening, or maybe even the 10-10-opening), then that's what I'll do. Give me a copy of AlphaGo and I'll be able to find a weakness somewhere.