<p></p>
<div>
<p>What distinguishes AI from traditional computer programs is that it can learn. In other words, you can train it to perform a specific task, and once this is completed, it can continue to self-educate itself without the need for manual inputs anymore. In this respect, it is possible to say that it learns “like a human”, but it learns much faster than a human, can learn from its mistakes, and perfect itself in a very short time. This is also true for games: you can train an AI to learn and develop game strategies too.</p>
<p>But how is this possible? For example, how does it add a new one to the <a target="_blank" href="https://roulette77.us/strategies/safe"><strong><u>minimal risk strategies</u></strong></a> that are available on the Roulette77 site? Let’s take a look at the answers to these questions and how artificial intelligence “learns”.</p>
<h2><strong>It starts with machine learning</strong></h2>
<p>Before AI can formulate a strategy, it needs to train itself. In this process, different machine learning algorithms are used, and they determine how the training process is handled. The most common <a target="_blank" href="https://www.ibm.com/think/topics/machine-learning"><strong><u>machine learning</u></strong></a> scenario works like this:</p>
<ul>
<li><strong>Supervised Learning</strong>: This algorithm is based on labeled examples and mainly consists of image recognition tasks. Thousands of images are shown to the AI so that it can recognize certain objects. For example, if training for chess, hundreds of photos of chess pieces with different designs taken from different angles will be used.</li>
<li><strong>Reinforcement Learning:</strong>The AI starts playing the game according to the basic rules and is rewarded when it makes ideal/optimal choices. This is a simple but highly effective reward-punishment system that improves the AI’s decision-making process over time.</li>
<li><strong>Deep Learning:</strong> AI creates sophisticated models by analyzing huge amounts of data and determines what the potential consequences of each decision will be. Using these results, it starts to develop strategies, building on what it has learned in the reward-punishment system mentioned above.</li>
</ul>
<p>This process is very different from, for example, a computer program like Deep Blue beating Garry Kasparov. Traditional programs like Deep Blue are not capable of “learning”. They lose against a human being who can think more flexibly than they can. But since artificial intelligence is constantly improving itself, it will not be possible to outthink it, especially if it is sufficiently advanced.</p>
<h2><strong>Then comes the strategies</strong></h2>
<p>Once the learning process is complete, the AI starts developing strategies and continues to do so continuously. In other words, it can analyze how effective a strategy is (without requiring manual input) and optimize it if it thinks this is necessary. Different techniques are used in this process:</p>
<ul>
<li><a target="_blank" href="https://builtin.com/machine-learning/monte-carlo-tree-search"><strong><u>Monte Carlo Tree Search</u></strong></a><strong>(MCTS):</strong> This is a technique that generates random simulations of complex games such as chess and go and places the results in a tree search. In this way, it is possible to simulate each branch of the tree separately and independently and create very deep simulations.</li>
<li><strong>Deep Neural Networks (DNN): </strong>This technique detects complex patterns from the data it analyzes and uses them to make intelligent decisions. It goes beyond just developing a strategy, it can also determine what moves the opponent will make with surprising consistency.</li>
<li><strong>Genetic Algorithms</strong>: This technique focuses on continuously analyzing and optimizing the results of previous simulations. This allows it to determine the best response to an unexpected move made by the opponent. As we will explain below, this is where artificial intelligence is weakest.</li>
</ul>
<p>All this could be interpreted to mean that this technology can develop almost perfect game strategies, but is this true? Can AI really be better than a human at game strategies?</p>
<h2><strong>Can AI be as good at game strategies as a human?</strong></h2>
<p>Unfortunately, there is no clear answer to this question. Technically, yes, AI can be much better at game strategies than a human because it learns much faster and more efficiently than we do. 99.9% of players cannot calculate how a decision they make will affect the game after 10 rounds, while the AI has completed more than a dozen simulations of what the game will look like after 100 rounds by the time you finish reading this sentence. Such processing power is impossible to deal with, no matter how good a player you are.</p>
<p>However, artificial intelligence can fail surprisingly badly when it comes to unexpected moves. Because it focuses on finding patterns during data analysis, it is not good at knowing what to do against a move that does not fit the available data set. A human can still beat it at this (and only this).</p>
<p>But it is important to remember that this technology never stops training itself. So, eventually, it will become much better than a human at everything (including unexpected moves). In short, in the near future, AI will be better than humans at every strategy in every game, regardless of its type.</p>
</p></div>
<p><a href="https://www.gistreel.com/how-does-artificial-intelligence-learn-and-create-game-strategies/" previewlistener="true">Source link </a></p>

