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And you do it again. But I'm going to explain today why it's not worth bothering to stop an examine at each move whether somebody has won. This should be a review. So we make all those moves and now, here's the unexpected finding by these people examining Go. So there's no way for the other player to somehow also make a path. No possible moves, no examination of alpha beta, no nothing. So it can be used to measure real world events, it can be used to predict odds making. Now you could get fancy and you could assume that really some of these moves are quite similar to each other. Okay, take a second and let's think about using random numbers again. You're going to do this quite simply, your evaluation function is merely run your Monte Carlo as many times as you can. And you're going to get some ratio, white wins over 5,, how many trials?

無料 のコースのお試し 字幕 So what does Monte Carlo bring click the table? You're not going to have to do a static evaluation on a leaf note where you can examine what the longest path is. And so there should be no advantage for a corner move over another corner move.

Here's our hex board, we're showing a five by five, so it's a relatively small hex board. I have to watch why do I have to be recall why I need to be in the double poker star monte carlo 2019.

Indeed, people do risk management using Monte Carlo, management of what's the case of getting a year flood or a year hurricane. You readily get abilities to estimate all sorts of things.

So you can use it heavily in investment. We've seen us doing a money color trial on dice games, on poker. That's going to be how you evaluate that board. We manufacture a probability by calling double probability. Filling out the rest of the board doesn't matter. Given how efficient you write your algorithm and how fast your computer hardware is.

So it's not truly random obviously to provide a large number of trials. The insight is you don't need two chess grandmasters or two hex grandmasters. All right, I have to be in the double domain because I want this to be double poker star monte carlo 2019.

And there poker star monte carlo 2019 be no advantage of making a move on the upper north side versus the lower south side.

So if I left out this, probability would always return 0.

Use a small board, make sure everything is working on a small board. So probabilistic trials can let us get at things and otherwise we don't have ordinary mathematics work. Rand gives you an integer pseudo random number, that's what rand in the basic library does for you. Critically, Monte Carlo is a simulation where we make heavy use of the ability to do reasonable pseudo random number generations. And in this case I use 1. It's int divide. The rest of the moves should be generated on the board are going to be random. How can you turn this integer into a probability? Turns out you might as well fill out the board because once somebody has won, there is no way to change that result. A small board would be much easier to debug, if you write the code, the board size should be a parameter. So you might as well go to the end of the board, figure out who won. And then by examining Dijkstra's once and only once, the big calculation, you get the result. Once having a position on the board, all the squares end up being unique in relation to pieces being placed on the board. That's what you expect. So here is a wining path at the end of this game. So here you have a very elementary, only a few operations to fill out the board. And we'll assume that white is the player who goes first and we have those 25 positions to evaluate. Why is that not a trivial calculation? This white path, white as one here. You can actually get probabilities out of the standard library as well. Because once somebody has made a path from their two sides, they've also created a block. But for the moment, let's forget the optimization because that goes away pretty quickly when there's a position on the board. And the one that wins more often intrinsically is playing from a better position. I've actually informally tried that, they have wildly different guesses. White moves at random on the board. 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. And these large number of trials are the basis for predicting a future event. Of course, you could look it up in the table and you could calculate, it's not that hard mathematically. But it will be a lot easier to investigate the quality of the moves whether everything is working in their program. And then, if you get a relatively high number, you're basically saying, two idiots playing from this move. So we could stop earlier whenever this would, here you show that there's still some moves to be made, there's still some empty places. It's not a trivial calculation to decide who has won. And that's a sophisticated calculation to decide at each move who has won. And we fill out the rest of the board. So we make every possible move on that five by five board, so we have essentially 25 places to move. Who have sophisticated ways to seek out bridges, blocking strategies, checking strategies in whatever game or Go masters in the Go game, territorial special patterns. And then you can probably make an estimate that hopefully would be that very, very small likelihood that we're going to have that kind of catastrophic event. Maybe that means implicitly this is a preferrable move. So black moves next and black moves at random on the board. Because that involves essentially a Dijkstra like algorithm, we've talked about that before. So it's not going to be hard to scale on it. You could do a Monte Carlo to decide in the next years, is an asteroid going to collide with the Earth. So it's a very useful technique. That's the answer. And that's now going to be some assessment of that decision. And that's the insight. So it's a very trivial calculation to fill out the board randomly. 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 here's a five by five board. We're going to make the next 24 moves by flipping a coin. And at the end of filling out the rest of the board, we know who's won the game. So you could restricted some that optimization maybe the value. I'll explain it now, it's worth explaining now and repeating later. That's the character of the hex game. One idiot seems to do a lot better than the other idiot. And we want to examine what is a good move in the five by five board. So what about Monte Carlo and hex? You'd have to know some facts and figures about the solar system. So for this position, let's say you do it 5, times. Instead, the character of the position will be revealed by having two idiots play from that position. You're not going to have to know anything else. And we're discovering that these things are getting more likely because we're understanding more now about climate change. 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. 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. So here's a way to do it. 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. But with very little computational experience, you can readily, you don't need to know to know the probabilistic stuff. Sometimes white's going to win, sometimes black's going to win. You'd have to know some probabilities.