blocks|key|4351912|text|以下可能会被证明是有用的：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4351913|4351914|The+University+of+Alberta+Computer+Poker+Research+Group|unordered-list-item|offset|length|4351915|OpenHoldem|4351916|Poker+Hand+Recognition,+Comparison,+Enumeration,+and+Evaluation|4351917|The+Theory+of+Poker|4351918|The+Mathematics+of+Poker|4351919|SpecialKPokerEval|4351920|4351921|entityMap|0|LINK|mutability|MUTABLE|url|http://poker.cs.ualberta.ca/|1|http://code.google.com/p/openholdembot/|2|http://www.codingthewheel.com/archives/how-i-built-a-working-online-poker-bot-8|3|https://rads.stackoverflow.com/amzn/click/com/1880685000|4|https://rads.stackoverflow.com/amzn/click/com/1886070253|5|http://code.google.com/p/specialkpokereval/^0|0|0|0|1J|0|0|0|A|1|0|0|1R|2|0|0|J|3|0|0|O|4|0|0|H|5|0|0^^$0|@$1|2|3|4|5|6|7|1A|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|1B|8|@]|9|@]|A|$]]|$1|C|3|D|5|E|7|1C|8|@]|9|@$F|1D|G|1E|1|1F]]|A|$]]|$1|H|3|I|5|E|7|1G|8|@]|9|@$F|1H|G|1I|1|1J]]|A|$]]|$1|J|3|K|5|E|7|1K|8|@]|9|@$F|1L|G|1M|1|1N]]|A|$]]|$1|L|3|M|5|E|7|1O|8|@]|9|@$F|1P|G|1Q|1|1R]]|A|$]]|$1|N|3|O|5|E|7|1S|8|@]|9|@$F|1T|G|1U|1|1V]]|A|$]]|$1|P|3|Q|5|E|7|1W|8|@]|9|@$F|1X|G|1Y|1|1Z]]|A|$]]|$1|R|3|-4|5|6|7|20|8|@]|9|@]|A|$]]|$1|S|3|-4|5|6|7|21|8|@]|9|@]|A|$]]]|T|$U|$5|V|W|X|A|$Y|Z]]|10|$5|V|W|X|A|$Y|11]]|12|$5|V|W|X|A|$Y|13]]|14|$5|V|W|X|A|$Y|15]]|16|$5|V|W|X|A|$Y|17]]|18|$5|V|W|X|A|$Y|19]]]]

The following may prove useful:

<ul>
<li><a href="http://poker.cs.ualberta.ca/" rel="noreferrer">The University of Alberta Computer Poker Research Group</a></li>
<li><a href="http://code.google.com/p/openholdembot/" rel="noreferrer">OpenHoldem</a></li>
<li><a href="http://www.codingthewheel.com/archives/how-i-built-a-working-online-poker-bot-8" rel="noreferrer">Poker Hand Recognition, Comparison, Enumeration, and Evaluation</a></li>
<li><a href="https://rads.stackoverflow.com/amzn/click/com/1880685000" rel="noreferrer" rel="nofollow noreferrer">The Theory of Poker</a></li>
<li><a href="https://rads.stackoverflow.com/amzn/click/com/1886070253" rel="noreferrer" rel="nofollow noreferrer">The Mathematics of Poker</a></li>
<li><a href="http://code.google.com/p/specialkpokereval/" rel="noreferrer">SpecialKPokerEval</a></li>
</ul>

blocks|key|4351893|text|众所周知，扑克AI很难做到正确，因为人类下注是不可预测的。它通常被分成两部分。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4351894|1)计算你的牌成为赢家的几率。|4351895|2)根据1制定投注策略。|4351896|我建议从阅读第一部分的大量统计数据开始。乍一看，这似乎很容易，但实际上它非常复杂(如果弄错了，你的人工智能将注定失败)。然后转到第二部分的遗传算法。投注策略主要是遗传算法。他们根据过去的成功和失败再加上一些随机性来调整自己，以免变得可预测。|4351897|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|J|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|K|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|L|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|M|8|@]|9|@]|A|$]]|$1|H|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|I|$]]

Poker AI's are notoriously difficult to get right because humans bet unpredictably. It's usually broken into two parts.

1) Calculate the odds of your hand being the winner.

2) Formulate betting strategy based on 1.

I'd recommend starting with lots of statistics reading for part 1. It seems easy at first blush, but it's actually very complicated (and getting it wrong will doom your AI). Then move on to genetic algorithms for part 2. Betting strategies are mostly genetic algorithms. They adjust themselves based on past success and failures + some randomization so as not to become predictable.

blocks|key|1011226|text|我用Java写了一个德州扑克视频扑克引擎|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1011227|这段代码是德州Hold‘’em的核心引擎，没有视图和其他|1011228|http://github.com/phstc/javapokertexasholdem|offset|length|1011229|entityMap|0|LINK|mutability|MUTABLE|url^0|0|0|0|18|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|P|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|Q|8|@]|9|@$F|R|G|S|1|T]]|A|$]]|$1|H|3|-4|5|6|7|U|8|@]|9|@]|A|$]]]|I|$J|$5|K|L|M|A|$N|E]]]]

I wrote a Texas Hold'em Video Poker engine in Java

This code is a core engine for Texas Hold'em without views and others

<a href="http://github.com/phstc/javapokertexasholdem" rel="nofollow">http://github.com/phstc/javapokertexasholdem</a>

blocks|key|4352064|text|同时，让遗传算法调整神经网络的权值，这决定了决策逻辑。这种方法非常适合扑克AI。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4352065|我自己做的人工智能就像这样。一开始，我创建了大约1000名玩家，他们根本不知道如何玩这个游戏。基于他们在手中的最初运气，他们的适应度被加权，并创造出新的一代。新的“大脑”比上一代玩得更好。|4352066|最终，最好的个人发挥得非常好。|4352067|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|H|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|I|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|J|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|G|$]]

Also, letting genetic algorithm adjust the weights of neural network, which determines the decision logic. This approach is very suitable for poker AI.

I made my own AI like this. At first, I created ~1000 players, who didn't know how to play the game at all. Based on their initial luck during the hands, their fitness was weighted and new generation created. New "brains" were playing better than previous generation.

Eventually, the best individuals played very good.

blocks|key|3504814|text|正如已经推荐的，的扑克理论这本书对于玩这个游戏以及构建AI来说是一个真正宝贵的信息来源。你应该买它，因为它不是很贵。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3504815|阿尔伯塔大学的研究小组目前正在进行最先进的研究，尽管他们时不时会出现激烈的竞争。(并非该领域的所有扑克机器人和人工智能研究都是公开的，因为在互联网扑克中使用自己的结果是很诱人的，尽管这是被禁止的。)|3504816|首先，你应该决定你要先解决哪种扑克。尽管最好的人类仍然与最好的AI进行真正的战斗，但两个玩家的hold‘’em基本上已经解决了。与人类相比，人工智能的主要优势在于对过去双手的无限完美记忆，对基于此的模式的完美分析，而且由于它们是机器，它们不会像几乎所有人类偶尔做的那样倾斜。|3504817|固定的hold‘’em可能是最容易破解的，所以你可能想从1-1固定hold‘’em开始，然后决定下一步要做什么。|3504818|以下是改变正确策略(和你的人工智能)的一些方面：|3504819|3504820|现金游戏不同于锦标赛-The玩家的数量决定了决定的不同。|unordered-list-item|3504821|Hold‘’em不是唯一的扑克牌奥马哈，斯图德和其他存在，并被广泛发挥。|3504822|Fixed+Limit不同于Pot+Limit，它不同于No+Limit。|3504823|要击败最好的你需要涵盖很多非常微妙的东西，最好的玩家在玩的时候会思考。要想击败低风险的业余游戏，这些都不算数。--|3504824|3504825|如果你决定选择无限制扑克，你可能想看看三本书系列和哈灵顿的Hold‘’em和一本书无限制扑克-理论和实践。在阅读了相当多关于扑克的书籍后，我可以说这些书结合了扑克理论已经足够了。|offset|length|style|BOLD|3504826|entityMap^0|0|0|0|0|0|0|0|0|0|0|0|15|B|0^^$0|@$1|2|3|4|5|6|7|12|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|13|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|14|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|15|8|@]|9|@]|A|$]]|$1|H|3|I|5|6|7|16|8|@]|9|@]|A|$]]|$1|J|3|-4|5|6|7|17|8|@]|9|@]|A|$]]|$1|K|3|L|5|M|7|18|8|@]|9|@]|A|$]]|$1|N|3|O|5|M|7|19|8|@]|9|@]|A|$]]|$1|P|3|Q|5|M|7|1A|8|@]|9|@]|A|$]]|$1|R|3|S|5|M|7|1B|8|@]|9|@]|A|$]]|$1|T|3|-4|5|6|7|1C|8|@]|9|@]|A|$]]|$1|U|3|V|5|6|7|1D|8|@$W|1E|X|1F|Y|Z]]|9|@]|A|$]]|$1|10|3|-4|5|6|7|1G|8|@]|9|@]|A|$]]]|11|$]]

As already recommended, the book Theory of Poker is a truly invaluable source of information for playing the game as well as for building an AI. You should probably buy it as it does not cost that much.

University of Alberta resarch group does the state-of-the-art at the moment, though they have stiff competition emerging every now and then. (Not all poker bots and AI research in the field is public because of the temptation to use one's results in internet poker, though that's forbidden.)

First you should decide what sort of poker are you going to tackle first. two player hold'em is pretty much solved, though the best humans still put up a real fight with the best AI's available. The AI has the main advantages over humans by having an unlimited flawless memory of past hands, flawless analysis of the patterns based on that and as they are machines, they don't tilt like almost all humans occasionally do.

Fixed Hold'em is probably the easiest to crack, so you might want to start with 1-1 fixed hold'em and then decide what you want to do next. 

Here are some aspects which change the correct strategy (and your AI):

<ul>
<li>A cash game is different from a
tournament 
-The number of players
makes the decisions different. </li>
<li>Hold'em
is not the only poker. Omaha, Stud
and others exist and are widely
played. </li>
<li>Fixed Limit is different from
Pot Limit, which is different from No
Limit.</li>
<li>To beat the best you need to cover a lot of very subtle things the best players think about when they play. To beat a low-stakes amateur game, none of these things count.</li>
</ul>

If you decide to go for No Limit Hold'em, you might want to check out three-book series Harrington on Hold'em and a book No Limit Poker - Theory and Practice. Having read quite a many books on poker, I can say these books combined with the Theory of Poker are quite enough.

blocks|key|3504970|text|我不确定您对哪个游戏感兴趣，但典型的方法是创建一个更小的游戏抽象版本，解决那个更小的游戏，然后将实际游戏情况映射回抽象游戏以生成建议。大多数学术论文跳过了这一过程的细节，倾向于呈现关于收敛、开发和竞争结果的结果。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3504971|然而，有一些公开可用的代码库提供了一个完整的实现。其中最好的一个是Fell：|3504972|http://www.deducer.org/pmwiki/pmwiki.php?n=Main.ArtificialIntelligencePoker|offset|length|3504973|这是一个基本的完整的策略机器人，它使用虚拟游戏来优化抽象游戏的策略。这是一个很好的起点，因为它相当简单、完整，并且很好地呈现了抽象游戏方法。|3504974|如果你对开发扑克AI感兴趣，我建议你阅读2007年及以后来自UA扑克组和Tuomas学生的所有内容：|3504975|http://www.cs.cmu.edu/~sandholm/|3504976|http://poker.cs.ualberta.ca/publications.html|3504977|entityMap|0|LINK|mutability|MUTABLE|url|1|2^0|0|0|0|23|0|0|0|0|0|W|1|0|0|19|2|0^^$0|@$1|2|3|4|5|6|7|Y|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|Z|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|10|8|@]|9|@$F|11|G|12|1|13]]|A|$]]|$1|H|3|I|5|6|7|14|8|@]|9|@]|A|$]]|$1|J|3|K|5|6|7|15|8|@]|9|@]|A|$]]|$1|L|3|M|5|6|7|16|8|@]|9|@$F|17|G|18|1|19]]|A|$]]|$1|N|3|O|5|6|7|1A|8|@]|9|@$F|1B|G|1C|1|1D]]|A|$]]|$1|P|3|-4|5|6|7|1E|8|@]|9|@]|A|$]]]|Q|$R|$5|S|T|U|A|$V|E]]|W|$5|S|T|U|A|$V|M]]|X|$5|S|T|U|A|$V|O]]]]

I'm not sure which exact game you are interested in, but the typical approach is to create a much smaller abstract version of the game, solving that smaller game, and then mapping real game situations back to the abstract game to generate advice. Most of the academic papers skip over the details of this process in favor of presenting results about convergence, exploitation, and competition results.

However, there are some publicly available code bases which present a complete implementation. One of the best ones is Fell Omen:

<a href="http://www.deducer.org/pmwiki/pmwiki.php?n=Main.ArtificialIntelligencePoker" rel="nofollow">http://www.deducer.org/pmwiki/pmwiki.php?n=Main.ArtificialIntelligencePoker</a>

This is a basic complete strategy bot that uses fictitious play to optimize the strategy for the abstract game. It's a good starting point because it is fairly straightforward, complete, and represents a good presentation of the abstract game approach.

If you are interested in developing poker AI, I would suggest reading everything from 2007 and on from the UA poker group and Tuomas Sandholm's students:

<a href="http://www.cs.cmu.edu/~sandholm/" rel="nofollow">http://www.cs.cmu.edu/~sandholm/</a>

<a href="http://poker.cs.ualberta.ca/publications.html" rel="nofollow">http://poker.cs.ualberta.ca/publications.html</a>

blocks|key|287469|text|我在大学的时候写了一篇关于AI的文章。它并不是特别先进，我使用了一个Q值机，它遍历了许多状态，并更新了每个状态的Q值。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|287470|我发现阿尔伯塔大学的AI扑克项目是避免陷阱的宝贵信息来源。|287471|正如上面的一张海报所述，第一步是确定几个可确定的扑克规则-可以通过编程开发一对一扑克。|287472|我陷入的一个陷阱是没有在早期构建可重构性。例如能够切换学习/游戏的等级。|287473|我很想知道你是怎么做的给我发邮件到gmail.com|287474|entityMap^0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|M|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|N|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|O|8|@]|9|@]|A|$]]|$1|H|3|I|5|6|7|P|8|@]|9|@]|A|$]]|$1|J|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|K|$]]

I wrote a Hold'Em AI in my undergrad. It wasn't particularly advanced, I used a Q-Value machine that traversed a number of states and updated Q values for each state.

I found the University of Alberta's AI Poker project an invaluable source of info for avoiding pitfalls. 

As one poster above states, the first step is to nail in a couple of determinable poker rules - one-on-one poker can be developed programatically.

One pitfall I fell into was not building in reconfigurability early on. For instance being able to switch the grade of learning/playing. 

I would be interested to hear how you get on drop me a mail stevekeogh at gmail.com

blocks|key|4352215|text|除了上面的链接，要实现的一个重要的事情是http://en.wikipedia.org/wiki/Kelly_criterion，它将帮助计算出给定一系列投注的预期赔率的最优投注大小。对于人类来说，在判断赔率时可能会有错误，但如果你的AI程序可以基于任何算法吐出一些预期的归一化赔率，那么这种平衡优势赌徒的风险和回报的赌注规模调整技术将是一个很好的廉价解决方案。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|4352216|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Kelly_criterion^0|K|18|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

Just to add to the links above, one of the important things to implement would be <a href="http://en.wikipedia.org/wiki/Kelly_criterion" rel="nofollow">http://en.wikipedia.org/wiki/Kelly_criterion</a> which will help figure out the optimal size of bets given the expected odds in a series of bets. 
With humans there could be errors in judgement of odds, but if your AI program can spit the some expected normalized odds based on whatever the algorithm then this bet-sizing technique which balances both risk and reward for the advantage gambler would be a good cheap solution.

blocks|key|4351972|text|我听说的一个有趣的结果是，如果您将投注选项限制为折叠、检查和全部下注，您可以编写一个概率至少为49%25的一对一获胜的人工智能，如果它(IIRC)不是第一个获胜的，则概率为49.5%25。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4351973|我不知道这个人工智能更容易写出知道下注多少的人工智能，但它值得深思:选择下注金额只占获胜概率的1.5%25。|4351974|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

One interesting result I've heard is that if you restrict the betting options to fold, check and all-in, you can write an AI that wins one-on-ones with probability at least 49%, and 49.5% if it's (IIRC) not going first.

I don't know that this AI is easier to write that one which knows how much to bet, but it's food for thought: choosing amounts to bet only accounts for 1.5% of the probability of winning.

I'm interested in building a Texas Hold 'Em AI engine in Java. This is a long term project, one in which I plan to invest at least two years. I'm still at college, haven't build anything ambitious yet and wanting to tackle a problem that will hold my interest in the long term. I'm new to the field of AI. From my data structures class at college, I know basic building blocks like BFS and DFS, backtracking, DP, trees, graphs, etc. I'm learning regex, studying for the SCJP and the SCJD and I'll shortly take a (dense) statistics course.

Questions:

-Where do I get started? What books should I pick? What kind of AI do poker playing programs run on? What open source project can I take a page from? Any good AI resources in Java? I'm interested in learning Lisp as well, is Jatha good?

Building a Texas Hold'em playing AI..from scratch

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我有兴趣在Java中构建一个德州Hold 'Em AI引擎。这是一个长期项目，我计划投资至少两年。我还在上大学，还没有建立任何雄心勃勃的东西，想要解决一个能让我长期保持兴趣的问题。我是AI领域的新手。从我在大学的数据结构课上，我知道了基本的构建块，如BFS和DFS，回溯，DP，树，图等。我正在学习正则表达式，学习SCJP和SCJD，我很快就会上一个(密集的)统计课程。问题：-Where我该开始了吗

问建立一个德州Hold‘’em玩AI..from scratch
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回答 9

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