My impression is that you don't understand how these things work, so I'll start from the basics; apologies if wrong. First off, the chess counter-example was i

My impression is that you don't understand how these things work, so I'll start from the basics; apologies if wrong. First off, the chess counter-example was in response to something general shmageggy said about 'all minimax algos'. More broadly, in practice the issue is that you only have computational resources to search a fraction of possible game states. So where do you direct your limited resources? Some candidate strategies: minimax with AB, exhaustive, MC sampling. Even with any of these strategies, we can usually only search the game state tree to a given depth. (This is the tree of 'If I go here, then the game goes there, then I go here etc'.) When we reach our depth limit (if we haven't reached a terminal state - i.e. a win or loss), we use a heuristic (maybe 'number of free tiles') which approximates the value of the state to us at that point. >Meanwhile, the computer has only a few possible moves, increasing the probability of doing something accidentally optimal. If the computer only has a few possible moves, then, yes, it might randomly choose the best one. But if it only has a few possible moves, chances are that any tree search technique, even stochastic, will start to expand all of them. The question is, though, how will you know how good each of these moves is when you start examining are? I.e. Which of the moves available to you right now should you make? With Minimax/AB, you'll get a sense the move is optimal, because you'll look at the consequences of the move, (if the game makes the worst response to me (if I make the best move for me (if the game makes the worst response for me ... (heuristic evaluation)))). With (sensible) MC search, you'll instead get a sense of which move is optimal by looking at more like what happens (if I make the best move for me (for each of a bunch of random moves the game could make (then if I make the best move for me (for each of a bunch of random moves the game could make ... (heuristic evaluation)))). My point is that the latter is more suitable for this domain. >As you approach the end, needing over half the board just for unbuilt path up to 1024 and thereby not having as much scratch space, the probability of it hitting a problematic (even if not "devastating", one that suddenly requires you to reorganize things to "clean up the mess") move seems more more of a problem than when playing chess. Well, then, if the probability of it randomly hitting a problematic move is high in a certain state, your MC search will be highly likely to come across that move, and thus you'll be likely to avoid moves that bring you to that state. So, no problem. In summary: 1) If the game is highly likely to make a devastating move by random chance, then you are highly likely to come across that move in your stochastic search, so that's not really an objection. 2) In this game, just as in chess, there are always more states you'd like to search and expand than you have the resources to. Even if the computer only has a handful of 'moves' at any time, in order to tell how good these moves around, you've still got to expand out a lot of states. Choosing to use minimax instead of MC doesn't save you from that.