Book Image

Unity 5.x Game AI Programming Cookbook

By : Jorge Palacios
5 (1)
Book Image

Unity 5.x Game AI Programming Cookbook

5 (1)
By: Jorge Palacios

Overview of this book

Unity 5 comes fully packaged with a toolbox of powerful features to help game and app developers create and implement powerful game AI. 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 practical Cookbook covers both essential and niche techniques to help you be able to do that and more. This Cookbook is engineered as your one-stop reference to take your game AI programming to the next level. Get to grips with the essential building blocks of working with an agent, programming movement and navigation in a game environment, and improving your agent's decision making and coordination mechanisms - all through hands-on examples using easily customizable techniques. Discover how to emulate vision and hearing capabilities for your agent, for natural and humanlike AI behaviour, and improve them with the help of graphs. Empower your AI with decision-making functions through programming simple board games such as Tic-Tac-Toe and Checkers, and orchestrate agent coordination to get your AIs working together as one.

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Unity 5.x Game AI Programming Cookbook
Jorge Palacios
Mar, 2016 | 278 pages
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Table of Contents (15 chapters)
Unity 5.x Game AI Programming Cookbook
Unity 5.x Game AI Programming Cookbook
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Behaviors – Intelligent Movement
Behaviors – Intelligent Movement
Introduction
Creating the behavior template
Pursuing and evading
Arriving and leaving
Facing objects
Wandering around
Following a path
Avoiding agents
Avoiding walls
Blending behaviors by weight
Blending behaviors by priority
Combining behaviors using a steering pipeline
Shooting a projectile
Predicting a projectile's landing spot
Targeting a projectile
Creating a jump system
2
Navigation
Navigation
Introduction
Representing the world with grids
Representing the world with Dirichlet domains
Representing the world with points of visibility
Representing the world with a self-made navigation mesh
Finding your way out of a maze with DFS
Finding the shortest path in a grid with BFS
Finding the shortest path with Dijkstra
Finding the best-promising path with A*
Improving A* for memory: IDA*
Planning navigation in several frames: time-sliced search
Smoothing a path
3
Decision Making
Decision Making
Introduction
Choosing through a decision tree
Working a finite-state machine
Improving FSMs: hierarchical finite-state machines
Combining FSMs and decision trees
Implementing behavior trees
Working with fuzzy logic
Representing states with numerical values: Markov system
Making decisions with goal-oriented behaviors
4
Coordination and Tactics
Coordination and Tactics
Introduction
Handling formations
Extending A* for coordination: A*mbush
Creating good waypoints
Analyzing waypoints by height
Analyzing waypoints by cover and visibility
Exemplifying waypoints for decision making
Influence maps
Improving influence with map flooding
Improving influence with convolution filters
Building a fighting circle
5
Agent Awareness
Agent Awareness
Introduction
The seeing function using a collider-based system
The hearing function using a collider-based system
The smelling function using a collider-based system
The seeing function using a graph-based system
The hearing function using a graph-based system
The smelling function using a graph-based system
Creating awareness in a stealth game
6
Board Games AI
Board Games AI
Introduction
Working with the game-tree class
Introducing Minimax
Negamaxing
AB Negamaxing
Negascouting
Implementing a tic-tac-toe rival
Implementing a checkers rival
7
Learning Techniques
Learning Techniques
.Introduction
Predicting actions with an N-Gram predictor
Improving the predictor: Hierarchical N-Gram
Learning to use Naïve Bayes classifiers
Learning to use decision trees
Learning to use reinforcement
Learning to use artificial neural networks
Creating emergent particles using a harmony search
8
Miscellaneous
Miscellaneous
Introduction
Handling random numbers better
Building an air-hockey rival
Devising a table-football competitor
Creating mazes procedurally
Implementing a self-driving car
Managing race difficulty using a rubber-banding system
Index
Index

Handling formations

This is a key algorithm for creating flocks or a group of military agents. It is designed to be flexible enough to give you the chance to create your own formations.

The end result from this recipe will be a set of target positions and rotations for each agent in the formation. Then, it is up to you to create the necessary algorithms to move the agent to the previous targets.

Note

We can use the movement algorithms learnt in Chapter 1, Movement, in order to target those positions.

Getting ready

We will need to create three base classes that are the data types to be used by the high-level classes and algorithms. The Location class is very similar to the Steering class and is used to define a target position and rotation given the formation's anchor point and rotation. The SlogAssignment class is a data type to match a list's indices and agents. Finally, the Character class component holds the target Location class.

The following is the code for the Location class:

using UnityEngine...
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