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This module shows how to use Monte Carlo evaluation in complex games such as Hex and Go. This had led top Apr 05, Highly recommended for anyone wanting to learn some serious C++ and introductory AI! やくに立ちましたか？ レッスンから 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. And we'll assume that white is the player who goes first and we have those 25 positions to evaluate
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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.