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Unity Artificial Intelligence Programming

Unity Artificial Intelligence Programming

By : Dr. Davide Aversa
4.8 (4)
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Unity Artificial Intelligence Programming

Unity Artificial Intelligence Programming

4.8 (4)
By: Dr. Davide Aversa

Overview of this book

Developing artificial intelligence (AI) for game characters in Unity has never been easier. Unity provides game and app developers with a variety of tools to implement AI, from basic techniques to cutting-edge machine learning-powered agents. Leveraging these tools via Unity's API or built-in features allows limitless possibilities when it comes to creating game worlds and characters. The updated fifth edition of Unity Artificial Intelligence Programming starts by breaking down AI into simple concepts. Using a variety of examples, the book then takes those concepts and walks you through actual implementations designed to highlight key concepts and features related to game AI in Unity. As you progress, you’ll learn how to implement a finite state machine (FSM) to determine how your AI behaves, apply probability and randomness to make games less predictable, and implement a basic sensory system. Later, you’ll understand how to set up a game map with a navigation mesh, incorporate movement through techniques such as A* pathfinding, and provide characters with decision-making abilities using behavior trees. By the end of this Unity book, you’ll have the skills you need to bring together all the concepts and practical lessons you’ve learned to build an impressive vehicle battle game.
Table of Contents (17 chapters)
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1
Part 1:Basic AI
6
Part 2:Movement and Navigation
11
Part 3:Advanced AI

Learning the basics of probability

There are many ways to define probability. The most intuitive definition of probability is called frequentism. According to frequentism, the probability of an event is the frequency with which the event occurs when we repeat the observation an infinite amount of times. In other words, if we throw a die 100 times, we expect to see a six, on average, 1/6th of the times, and we should get closer and closer to 1/6th with 1,000, 10,000, and 1 million throws.

We can write the probability of event A occurring as P(A). To calculate P(A), we need to know all the possible outcomes (N) for the observation and the total number of times in which the desired event occurs (n).

We can calculate the probability of event A as follows:

If P(A) is the probability of event A happening, then the probability of event A not happening is equal to the following:

The probability must be a real number between zero and one. Having a probability of zero means...

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