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How Does Artificial Intelligence Master the Playing of Card Games?

Artificial intelligence has gradually progressed over the years to a stage where it is now capable of some astonishing feats. When Deep Blue managed to prevail against then reigning world chess champion Garry Kasparov in 1996, it showed the awesome potential of computers. Since that time, AI has advanced so much that the initial ground-breaking machine is now nowhere near the top when it comes to chess engines. AI has also taken on the world of cards, and machines have been able to self-teach games to an extent that they can comfortably defeat human players. The big question for many people is, how is this done?

AI Has Recently Shown its Prowess With Cards

While it may not be mainstream news yet, the prowess of AI is well-known among card players. It has been used for almost every card game since computers began, with the help of random number generators to make the deals random. But, until recently, AI hadn't really played the games itself at a level worthy of professionals. This has changed in the last few years, though, as AI machines have been built to take on the world's best at poker.

Poker is considered to be a game that is predominantly decided by skill, rather than the turn of the cards. And because no outcome is set in stone, AI can't play out exact pathways towards an end result as it can in chess. Even so, AI has been able to learn the most optimal ways of playing poker so that it has the greatest possible chance of winning.

One of the best-known examples of AI showing its prowess with cards was when Pluribus became the first robot to beat experts in a multiplayer game of Texas Hold'em. Chris Ferguson said that the machine created by Facebook's AI Lab and Carnegie Mellon University was extremely hard to play against. The interesting thing about Pluribus was that it decided to go against the commonly held practice of limping in and preferred donk betting.

Programming Machines so That They Can Self Learn

It seems strange that a machine that was created by humans can surpass their ability. It would be intuitive to think that AI could only reach the stage of its creators and not be able to go any higher than that. But this isn't how the machines are programmed. Instead, they are built in such a way that they can learn how to play the games themselves, by testing millions of different possibilities over and over again. A human could learn a game in the same way, but what the computer can do in the space of a few hours would take a person hundreds of years.

Pluribus built its own base strategy, which is tested against itself in offline play and against opponents in real-time play. In the early stages of its development, it made a high number of errors, but remembered each and every one of these and tested out new approaches in the same scenarios to see how the results differed. This has enabled it to draw on probabilities to make the most optimal decisions in any situation.

Another card fanatic reported on how he devised an algorithm to learn a German card game. He chose a simple to learn but difficult to master offering, and created an algorithm using data that he collected through a four-handed simulation process. The AI then goes on to test new models against its existing data, constantly evaluating the process to see what the best lines of play are.

How Can Human Players Learn From This Process?

The fact that AI has proven that it is possible to master card games through constant practice and review of past mistakes should give card players hope that they can improve at their game of choice. Blackjack is an example of a game that many would like to master, as it is one of the most popular card games in the world. Taking inspiration from AI, it would be best to start with free online blackjack rather than real money. The free version is the perfect way to learn the rules of the game, and there is no risk of losing money. It's also helpful for learning new blackjack variants. Players can learn the game for free first, and then take their expertise to cash games. Along with this, there are various tips and explanations for new players too.

This method can be used for several different card games, with poker being another excellent option as well. Poker players who want to rise to the upper echelons of the game need to spend a long time practising and perfecting their strategies. If they do this in free games first, there is much less risk involved.

The code that programmers use to develop these AI card playing machines is complex but, simply put, it allows the bot to learn the game. From that base knowledge, it can run through millions of different scenarios to develop a playing style that has the best chance of winning. Humans can replicate this, but it would take a lot longer.


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