Book Image

Unity Artificial Intelligence Programming - Fourth Edition

By : Dr. Davide Aversa, Aung Sithu Kyaw, Clifford Peters
Book Image

Unity Artificial Intelligence Programming - Fourth Edition

By: Dr. Davide Aversa, Aung Sithu Kyaw, Clifford Peters

Overview of this book

Developing Artificial Intelligence (AI) for game characters in Unity 2018 has never been easier. Unity provides game and app developers with a variety of tools to implement AI, from the 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 your game's worlds and characters. This fourth edition with Unity will help you break down AI into simple concepts to give you a fundamental understanding of the topic to build upon. 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. Further on, you'll learn how to distinguish the state machine pattern and implement one of your own. This is followed by learning how to implement a basic sensory system for your AI agent and coupling it with a Finite State Machine (FSM). Next, you'll learn how to use Unity's built-in NavMesh feature and implement your own A* pathfinding system. You'll then learn how to implement simple ?ocks and crowd dynamics, which are key AI concepts in Unity. Moving on, you'll learn how to implement a behavior tree through a game-focused example. Lastly, you'll apply all the concepts in the book to build a popular game.
Table of Contents (13 chapters)

FSM with probability

We discussed FSM in Chapter 2, Finite State Machines, using both simple switch statements and the FSM framework. The decision to choose which state to execute was purely based on the true or false value of a given condition. Cast your mind back to the following FSM of our AI-controlled tank entity:

Tank AI FSM

To make the AI more interesting, and a little bit unpredictable, we can give our tank entity some options to choose from, instead of doing the same thing whenever a certain condition is met. For example, in our earlier FSM, our AI tank would always chase the player tank once the player was in its line of sight. Instead, we can split the player on sight transaction in order to connect a new state, Flee. How can the AI decide which state to move to? Randomly, of course:

FSM using probability

As shown in the preceding diagram, instead of chasing every...