AI Gaming & Machine Learning

AI Gaming & Machine Learning

Ages: 9-11*

Course Overview:

Have you ever wondered…

  • How Alexa can understand and respond to you?
  • How enemies in video games can work together to try and beat the player?
  • Or how Google can guess what you are searching for before you finish typing?

In this course, students learn introductory concepts of Machine Learning and Artificial Intelligence. Kids will use these concepts (including augmented reality, simulated learning and functions, and AI behaviors) to create fast-paced interactive games such as Fruit Ninja, Chatbots, Rock/Paper/Scissors, and AI Pets!

Key Skills students will build in class:
1. Learning programming concepts that apply to interactive gaming
2. Understanding the basic concepts of what machine learning is through designing games that use it
3. Creating advanced characters that utilize behaviors that emulate Artificial Intelligence

If you are wondering what all the buzz is about, here is a quick primer:

Artificial Intelligence: The idea that a computer program/machine can think and learn to be able to carry out tasks in a “smart” way.

Machine Learning: This gives computers the ability to learn without being explicitly told how to do so. We give the machines access to data and let them use the data to learn for themselves!

*These are recommended age ranges. We can adjust based on your child’s needs and abilities.

AI Gaming and Machine Learning is Fun for Kids

Artificial Intelligence imitates human intelligence processes using machines and has played a significant role in sci-fi for years. Some years ago, self-driving cars and robotic helpers were limited to the fantasy world, but now they have become a reality. Most importantly, helpful apps like Roomba and Google Maps, and even online shopping chatbots all use AI. In short, Al has become an essential part of every industry, including tourism, healthcare and more.

Industries that are using Al are already earning profits. Moreover, Al offers opportunities not only for industry professionals but also for kids. Let's view the two subcategories of Al, machine learning and Al gaming. We will explore how they are useful for kids. The first step is learning how to code.

Machine Learning is Enjoyable for Kids

Machine learning is a subfield of artificial intelligence. Engineers write algorithms that enable computers to learn from data. Besides, it allows computers to make predictions and act without clear instructions for every step. When you use search engines like Google and websites and apps like Amazon and Facebook, they suggest the best content. How does it all happen? These websites and apps also rely on machine learning. In the computer programming world, data plays a significant role.

There is no doubt that machine learning is one of the most lucrative and vital industries. The essential skills that machine learning engineers must have include expertise in Python, Java, and JavaScript; even kids can learn these languages. In addition, introductory concepts of machine learning are also easy-to-learn for kids, like simulated learning and algorithms, data structures, and functions. However, the first step for kids is to take machine learning coding classes that enable them to explore exciting future opportunities.

Al Gaming for Kids is Fun

The use of machine learning in video gaming creates an interesting experience. The video games include non-player characters known as 'bots.' It looks like another game player is controlling these bots when Al is controlling their behavior. In this way, the gaming experience becomes more exciting because players think their opponents are actually other people. Al has extensive use in video gaming, including online chess and poker tournaments and the most famous games like The Last of Us and Pokemon Go.
The machine learning algorithm used in gaming has two basic types, including Finite State Machine and Monte Carlo Search Tree.

Finite State Machine

When writing an FSM algorithm, game developers consider all possible states that a bot can be in and the probable events they are likely to face. Hence, they design specific responses for each situation. For instance, they write about the bot work process when it encounters a human player. But the algorithm is simple, and it makes the gaming experience repetitive and bots' actions easy to predict. So, developers created Monte Carlo Search Tree to solve this problem.

Monte Carlo Search Tree

In contrast, MCST is a complex algorithm developed in 2006; it was a milestone in machine learning. Bots with MCST programming do not only respond to a certain number of initial states and remain in a simple loop. Developers program bots with MCST in a way that they identify human players’ responses. In addition, it determines the possible outcomes that can take place and focus on the best outcomes for the bot's success.
The algorithm develops a tree of possible scenarios, responses, and outcomes that are likely to occur; it’s like a neural network in the bot's brain. This way, it offers a vast number of potential scenarios that make the game more exciting for players.

Additionally, Al gaming is changing with the help of Augmented Reality and Virtual Reality. Augmented Reality projects the game world into reality through the screen of a mobile device. On the other hand, Virtual Reality includes wearing a headset that transports the gamer inside the game. Al games have taken the gaming world by storm and are becoming popular among kids. Interestingly, the next generation of AI game developers will create more interactive and exciting gaming experiences.

While it all sounds exciting, it also has a link with education. Learning to code will open up ways to become the next big Al game developer. Kids can also learn the essential programming concepts of interactive gaming, machine learning, Al behaviors, and Augmented Reality. It will enable them to develop fast-paced, interactive games that they can share with friends. Al gaming coding classes open many opportunities for young video gamers.

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