Book Image

Unity 2018 Artificial Intelligence Cookbook - Second Edition

By : Jorge Palacios
Book Image

Unity 2018 Artificial Intelligence Cookbook - Second Edition

By: Jorge Palacios

Overview of this book

Interactive and engaging games come with intelligent enemies, and this intellectual behavior is combined with a variety of techniques collectively referred to as Artificial Intelligence. Exploring Unity's API, or its built-in features, allows limitless possibilities when it comes to creating your game's worlds and characters. This cookbook covers both essential and niche techniques to help you take your AI programming to the next level. To start with, you’ll quickly run through the essential building blocks of working with an agent, programming movement, and navigation in a game environment, followed by improving your agent's decision-making and coordination mechanisms – all through hands-on examples using easily customizable techniques. You’ll then discover how to emulate the vision and hearing capabilities of your agent for natural and humanlike AI behavior, and later improve the agents with the help of graphs. This book also covers the new navigational mesh with improved AI and pathfinding tools introduced in the Unity 2018 update. You’ll empower your AI with decision-making functions by programming simple board games, such as tic-tac-toe and checkers, and orchestrate agent coordination to get your AIs working together as one. By the end of this book, you’ll have gained expertise in AI programming and developed creative and interactive games.
Table of Contents (12 chapters)

Implementing Rock-Paper-Scissors AI with UCB1

Rock-Paper-Scissors is a classic game for testing AI techniques; that's why we'll use this case scenario for the current and following recipes. We will implement what are called bandit algorithms based on the notion of exploring n-armed bandits. It's usually modeled towards a slot machine, but we will study it as an RPS player. The main idea is to get hold of the option that results in a better payoff.

In this recipe, we will learn about the UCB1 algorithm and how it works.

Getting ready...

First, we need to implement a data structure for defining our actions:

public enum RPSAction
{
Rock, Paper, Scissors
}
...