Specifically take a look at Part 8 which goes into detail on how to evaluate hand value. What you'll want to do is statistically evaluate the cards in the hand and from there determine which cards have the highest probability of scoring a winning hand. You can then discard the cards you don't need.
If you are looking to simply build a poker playing program, then the above suggestions are helpful. If you are looking to build a system that learns how to play poker, then you need to make use of machine learning/inductive learning techniques such as neural networks, decision trees, genetic algorithms. Samuel's wrote an early checkers playing program that learned to improve it's performance.
You are going to want to research Expert Systems Design. And apply it to your problem domain.
If it seems overwhelming, start small. Create a rudimentary knowledge base of rules that make your poker bot greedily seek out pairs (and 3 or 4 of a kind). When that is doing well, add in rules to try to build straights, and rules on when to favor straight building over pair seeking, then when that works well, add in flushes. Continual, and small incrementation is the key to the process.
The things you want to study are game theory and data mining. The top programs use a large game theoretic simulation to produce tolerances for an expert system which is employed to randomize play in such a way that it makes as few systematic mistakes as possible. If you search the web well enough, you'll find full implementations of a few of these types of solutions, but they are often lacking the code that generated them. The down side to just accepting one of these solutions is that they do not take full advantage of the mistakes of opponents, which is where most of today's research is headed on the topic.
There are a number of ways to produce game theoretic solutions however, using just about every imaginable AI algorithm.
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看看我如何构建一个可用的扑克机器人系列< /a> （部分1，2, 3，4，5, 6, 7, 8)，其中包含有关扑克机器人入门的重要信息。
具体请查看第 8 部分 详细介绍了如何评估手牌价值。 您需要做的是统计评估手中的牌，并从中确定哪些牌最有可能赢得胜利。 然后您可以丢弃不需要的卡。
Take a look at How I Built a Working Poker Bot Series (Parts 1, 2, 3, 4, 5, 6, 7, 8) which has great information for getting started in poker botting.
Specifically take a look at Part 8 which goes into detail on how to evaluate hand value. What you'll want to do is statistically evaluate the cards in the hand and from there determine which cards have the highest probability of scoring a winning hand. You can then discard the cards you don't need.
联系阿尔伯塔大学怎么样
电脑扑克研究小组？
How about getting in touch with The University of Alberta
Computer Poker Research Group?
如果您只是想构建一个扑克游戏程序，那么上述建议很有帮助。 如果您希望构建一个学习如何玩扑克的系统，那么您需要利用机器学习/归纳学习技术，例如神经网络、决策树、遗传算法。 塞缪尔编写了一个早期的跳棋游戏程序，该程序学会了提高其性能。
http://en.wikipedia.org/wiki/Arthur_Samuel
机器上还有一个站点学习和游戏：
http://satirist.org/learn-game/
If you are looking to simply build a poker playing program, then the above suggestions are helpful. If you are looking to build a system that learns how to play poker, then you need to make use of machine learning/inductive learning techniques such as neural networks, decision trees, genetic algorithms. Samuel's wrote an early checkers playing program that learned to improve it's performance.
http://en.wikipedia.org/wiki/Arthur_Samuel
There is also a site on machine learning and games:
http://satirist.org/learn-game/
您将需要研究专家系统的设计。 并将其应用到您的问题域。
如果看起来势不可挡，那就从小事做起。 创建一个基本的规则知识库，使您的扑克机器人贪婪地寻找对（以及 3 或 4 个同类）。 当效果良好时，添加尝试建立顺子的规则，以及何时支持顺子构建而不是寻求对子的规则，然后当效果良好时，添加同花。 持续的、小幅的增量是这个过程的关键。
You are going to want to research Expert Systems Design. And apply it to your problem domain.
If it seems overwhelming, start small. Create a rudimentary knowledge base of rules that make your poker bot greedily seek out pairs (and 3 or 4 of a kind). When that is doing well, add in rules to try to build straights, and rules on when to favor straight building over pair seeking, then when that works well, add in flushes. Continual, and small incrementation is the key to the process.
你想要研究的是博弈论和数据挖掘。 顶级程序使用大型博弈论模拟来为专家系统产生容差，该系统用于随机化游戏，从而尽可能减少系统性错误。 如果您在网络上搜索得足够好，您会发现其中一些类型的解决方案的完整实现，但它们通常缺少生成它们的代码。 仅仅接受其中一种解决方案的缺点是，它们没有充分利用对手的错误，而这正是当今大多数研究的主题。
然而，产生博弈论解决方案的方法有很多种，几乎可以使用所有可以想象到的人工智能算法。
The things you want to study are game theory and data mining. The top programs use a large game theoretic simulation to produce tolerances for an expert system which is employed to randomize play in such a way that it makes as few systematic mistakes as possible. If you search the web well enough, you'll find full implementations of a few of these types of solutions, but they are often lacking the code that generated them. The down side to just accepting one of these solutions is that they do not take full advantage of the mistakes of opponents, which is where most of today's research is headed on the topic.
There are a number of ways to produce game theoretic solutions however, using just about every imaginable AI algorithm.
检查扑克评估器。 http://www.codingthewheel.com/archives/poker-hand-evaluator-综述
使用蒙特卡洛分析来找出按顺序排出哪些牌以最大化期望。
Check poker evaluator. http://www.codingthewheel.com/archives/poker-hand-evaluator-roundup
Use monte carlo analysis to find which cards to discharged in oreder to maximise expectations.
计算机游戏技巧 作者：David Levy
涵盖螺柱和抽拉。 当你读完之后你就会意识到它有多难。
请注意，这本书已经有 25 多年的历史了，但它是一个好的开始。
Computer Gamesmanship by David Levy
Covers stud and draw. You'll realize how hard it is by the time you're done reading.
Note that the book is over 25 years old, but it's a good start.