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Unity 5.x Game AI Programming Cookbook

You're reading from   Unity 5.x Game AI Programming Cookbook Build and customize a wide range of powerful Unity AI systems with over 70 hands-on recipes and techniques

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Product type Paperback
Published in Mar 2016
Publisher Packt
ISBN-13 9781783553570
Length 278 pages
Edition 1st Edition
Tools
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Authors (2):
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Jorge Palacios Jorge Palacios
Author Profile Icon Jorge Palacios
Jorge Palacios
Jorge Elieser P Garrido Jorge Elieser P Garrido
Author Profile Icon Jorge Elieser P Garrido
Jorge Elieser P Garrido
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Toc

Table of Contents (10) Chapters Close

Preface 1. Behaviors – Intelligent Movement FREE CHAPTER 2. Navigation 3. Decision Making 4. Coordination and Tactics 5. Agent Awareness 6. Board Games AI 7. Learning Techniques 8. Miscellaneous Index

Predicting actions with an N-Gram predictor


Predicting actions is a great way to give players a challenge by going from random selection to selection based on past actions. One way to implement learning is by using probabilities in order to predict what the player will do next, and that's what an N-Gram predictor does.

To predict the next choice, N-Gram predictors hold a record of the probabilities of making a decision (which is usually a move), given all combinations of choices for the previous n moves.

Getting ready…

This recipe makes use of general types. It is recommended that we have at least a basic understanding of how they work because it's critical that we use them well.

The first thing to do is implement a data type for holding the actions and their probabilities; we'll call it KeyDataRecord.

The KeyDataReconrd.cs file should look like this:

using System.Collections;
using System.Collections.Generic;

public class KeyDataRecord<T>
{
    public Dictionary<T, int> counts;
...
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