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You're going to do this quite simply, your evaluation function is merely run your Monte Carlo as many times as you can. And that's now going to be some assessment of that decision. This should be a review. 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 probabilistic trials can let us get at things and otherwise we don't have ordinary mathematics work. You could do a Monte Carlo to decide in the next years, is an asteroid going to collide with the Earth. Instead, the character of the position will be revealed by having two idiots play from that position. Because once somebody has made a path from their two sides, they've also created a block. And you do it again. Sometimes white's going to win, sometimes black's going to win. And that's the insight. So for this position, let's say you do it 5, times. This white path, white as one here. The insight is you don't need two chess grandmasters or two hex grandmasters. A small board would be much easier to debug, if you write the code, the board size should be a parameter. I'll explain it now, it's worth explaining now and repeating later. Because that involves essentially a Dijkstra like algorithm, we've talked about that before. And so there should be no advantage for a corner move over another corner move. Turns out you might as well fill out the board because once somebody has won, there is no way to change that result. Use a small board, make sure everything is working on a small board. 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. Filling out the rest of the board doesn't matter. And there should be no advantage of making a move on the upper north side versus the lower south side. And you're going to get some ratio, white wins over 5,, how many trials? So it's a very useful technique. You can actually get probabilities out of the standard library as well. And we fill out the rest of the board. You're not going to have to know anything else. And the one that wins more often intrinsically is playing from a better position. That's going to be how you evaluate that board. So there's no way for the other player to somehow also make a path. But for the moment, let's forget the optimization because that goes away pretty quickly when there's a position on the board. We've seen us doing a money color trial on dice games, on poker. And in this case I use 1. Okay, take a second and let's think about using random numbers again. 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 at the end of filling out the rest of the board, we know who's won the game. One idiot seems to do a lot better than the other idiot. It's int divide. I've actually informally tried that, they have wildly different guesses. So here is a wining path at the end of this game. You readily get abilities to estimate all sorts of things. How can you turn this integer into a probability? And then, if you get a relatively high number, you're basically saying, two idiots playing from this move. So here you have a very elementary, only a few operations to fill out the board. So what about Monte Carlo and hex? Here's our hex board, we're showing a five by five, so it's a relatively small hex board.

無料 のコースのお試し 字幕 So what does Monte Carlo read more to the table? Of course, you could look it up in the table and you could calculate, skrill verification 2019 not that hard mathematically.

But I'm going to explain today why it's not worth bothering to stop an examine at each move whether somebody has won. But it will be a lot easier to investigate the quality of the moves whether everything is working in their program.

And indeed, when you go to write your code and hopefully I've said this already, don't use the poker star monte carlo 2019 boards right off the bat.

So it's not truly random obviously to provide a large number of trials. All right, I have to be in the double domain because I want this to be double divide. That's the character of the hex game. Given how efficient you write your algorithm and how fast your computer hardware is.

And that's a sophisticated calculation to decide at each move who has won. So if I left out this, probability would always return 0.

Maybe that means implicitly this is a preferrable move. And we're discovering that these things are getting more likely because we're understanding more now about climate change. 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. It's not a trivial calculation to decide who has won. So here's a five by five board. You'd have to know some probabilities. So you could restricted some that optimization maybe the value. So you can use it heavily in investment. Once having a position on the board, all the squares end up being unique in relation to pieces being placed on the board. We manufacture a probability by calling double probability. We're going to make the next 24 moves by flipping a coin. And we want to examine what is a good move in the five by five board. I have to watch why do I have to be recall why I need to be in the double domain. 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. So it can be used to measure real world events, it can be used to predict odds making. You'd have to know some facts and figures about the solar system. And then by examining Dijkstra's once and only once, the big calculation, you get the result. So you might as well go to the end of the board, figure out who won. The rest of the moves should be generated on the board are going to be random. Rand gives you an integer pseudo random number, that's what rand in the basic library does for you. So it's a very trivial calculation to fill out the board randomly. Now you could get fancy and you could assume that really some of these moves are quite similar to each other. 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. Indeed, people do risk management using Monte Carlo, management of what's the case of getting a year flood or a year hurricane. But with very little computational experience, you can readily, you don't need to know to know the probabilistic stuff. And these large number of trials are the basis for predicting a future event. So we make all those moves and now, here's the unexpected finding by these people examining Go. No possible moves, no examination of alpha beta, no nothing. That's what you expect. That's the answer. 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. White moves at random on the board. 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. Why is that not a trivial calculation? Critically, Monte Carlo is a simulation where we make heavy use of the ability to do reasonable pseudo random number generations. So here's a way to do it. So black moves next and black moves at random on the board. So it's not going to be hard to scale on it. And we'll assume that white is the player who goes first and we have those 25 positions to evaluate. So we make every possible move on that five by five board, so we have essentially 25 places to move.