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.
Table of Contents (15 chapters)
Unity 5.x Game AI Programming Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Blending behaviors by weight


Blending techniques allow you to add behaviors and mix them without creating new scripts every time you need a new type of hybrid agent.

This is one of the most powerful techniques in this chapter, and it's probably the most used behaviour-blending approach because of its power and the low cost of implementation.

Getting ready

We must add a new member variable to our AgentBehaviour class called weight and preferably assign a default value—in this case, 1.0f. Besides this, we should refactor the Update function to incorporate weight as a parameter to the Agent class' SetSteering function. All in all, the new AgentBehaviour class should look something like this:

public class AgentBehaviour : MonoBehaviour
{
    public float weight = 1.0f;

    // ... the rest of the class

    public virtual void Update ()
    {
        agent.SetSteering(GetSteering(), weight);
   }
}

How to do it...

We just need to change the SetSteering agent function's signature and definition:

public void SetSteering (Steering steering, float weight)
{
    this.steering.linear += (weight * steering.linear);
    this.steering.angular += (weight * steering.angular);
}

How it works...

The weights are used to amplify the steering behavior result, and they're added to the main steering structure.

There's more...

The weights don't necessarily need to add up to 1.0f. The weight parameter is a reference for defining the relevance that the steering behavior will have among the other ones.

See also

In this project, there is an example of avoiding walls, worked out using weighted blending.