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無料 のコースのお試し 字幕 So what does Monte Carlo bring to the table? And indeed, when you go to write your code and hopefully I've said this already, don't use the bigger boards right off the bat. So if I left out this, probability would always return 0.
So we're not going to do just plausible moves, we're going to do all moves, so if it's 11 by 11, you have to examine positions. So it's not truly random obviously to provide a large number of trials. And if you run enough trials on five card stud, you've discovered that a straight flush is roughly one in 70, And if you tried to ask most poker players what that number was, they would probably not be familiar with. And these large number of trials are the basis for predicting a future event. You're not going to have to know anything else. So it's not going to be hard to scale on it. So you can use it heavily in investment. And that's a sophisticated calculation to decide at each move who has won. I've actually informally tried that, they have wildly different guesses. So there's no way for the other player to somehow also make a path. Filling out the rest of the board doesn't matter. Why is that not a trivial calculation? Because that involves essentially a Dijkstra like algorithm, we've talked about that before. So it's a very trivial calculation to fill out the board randomly. That's what you expect. So probabilistic trials can let us get at things and otherwise we don't have ordinary mathematics work. So black moves next and black moves at random on the board. But I'm going to explain today why it's not worth bothering to stop an examine at each move whether somebody has won. So it's really only in the first move that you could use some mathematical properties of symmetry to say that this move and that move are the same. So here's a way to do it. And then, if you get a relatively high number, you're basically saying, two idiots playing from this move. And we want to examine what is a good move in the five by five board. White moves at random on the board. Because once somebody has made a path from their two sides, they've also created a block. Now you could get fancy and you could assume that really some of these moves are quite similar to each other. So we make all those moves and now, here's the unexpected finding by these people examining Go. And that's now going to be some assessment of that decision. You'd have to know some probabilities. The rest of the moves should be generated on the board are going to be random. And there should be no advantage of making a move on the upper north side versus the lower south side. So here is a wining path at the end of this game. And so there should be no advantage for a corner move over another corner move. Sometimes white's going to win, sometimes black's going to win. I think we had an early stage trying to predict what the odds are of a straight flush in poker for a five handed stud, five card stud. It's not a trivial calculation to decide who has won. Maybe that means implicitly this is a preferrable move. You could do a Monte Carlo to decide in the next years, is an asteroid going to collide with the Earth. You readily get abilities to estimate all sorts of things. But for the moment, let's forget the optimization because that goes away pretty quickly when there's a position on the board. You can actually get probabilities out of the standard library as well.