Machine Learning in Gaming Is Smarter than Before

There are many ways to include machine learning in video games, and we now see the expansion of this field in the video game industry. More and more games rely on machine learning in gaming, and the game players are thrilled to see this technology in action. Machine learning is part of artificial intelligence, and the application ranges from Non-Player Character (NPC) control to Procedural Content Generation (PCG). 

In the past, the NPCs were based on a set of rules coded by humans. They depended on the codes that were written by programmers and their actions were very predictable. In the future, the NPCs will program and reprogram their own rules, which leaves more space for the authentic experiences of the players. The characters will program their actions according to the gamers’ experiences and this will happen in the process of playing games. The more the game is played, the more machine learning actions will happen behind the curtain. 

Machine learning in game development is based on artificial intelligence models that use algorithms and statistical models to make machines act without the programming methods. This is visible in many strategy games where the characters are diverse and able to compete with humans. We can also see this implementation in chess games and the Go game. 

Some of the relevant machine learning techniques include the following aspects: 

• Deep learning – It is an aspect of machine learning that uses artificial neural networks in order to perform complex tasks. It uses input and extracts the information from it. This technique requires powerful machines that will be used for running the deep learning possibilities. 
• Recurrent neural network – It is part of artificial neural networks that process data in a selected order. It processes data one part at a time, not all at once. The output is designed according to the memory of the previous sequence. 
• Reinforcement learning – It is the process of machine learning that uses rewards and punishments. An agent gets a positive reward when winning the game, and there is a punishment when losing the game. This process is important in many games and in the use of robotics. 

Other machine learning techniques include additional aspects such as convolutional neural networks, long short-term memory, and neuroevolution. All these technologies are made to provide the response in games and robotics. Many game players can see the importance of machine learning which is visible in many games. 

Some of the games with machine learning include the following games:

• Chess – This is a strategy game that uses AI agents to beat human players. There are many algorithms that are used in this game, and we all know about the match between Deep Blue and Garry Kasparov in 1997. 
• Go – It is another strategy-based game that is even more complicated than chess. The algorithms are more complex and the machine learning agents are able to play at the amateur human level when it comes to the competition. 
• StarCraft series – It is one of the games that uses machine learning and this real-time strategy game has been popular for AI research. There are various deep learning methods that were tested in this game in order to make it more appealing and more difficult for human players. 

Some other popular AI learning games include a wide variety of games. Most of them are strategy games, but there are also other kinds of AI-based games. The list continues and it includes games like Dota 2, Supreme Commander 2, Planetary Annihilation, and many more. 

It is obvious that machine learning in gaming is more advanced and more popular than before, which is why many game developers use AI techniques to develop games that will be interesting and complex enough for human players.