Procedural content generation via machine learning : an overview / Matthew Guzdial, Sam Snodgrass, Adam J. Summerville.

Format:

Book

Author/Creator:

Guzdial, Matthew, author.

Snodgrass, Sam, author.

Summerville, Adam J., author.

Series:

Synthesis Lectures on Games and Computational Intelligence

Language:

English

Subjects (All):

Machine learning.

Physical Description:

1 online resource (246 pages)

Edition:

1st ed.

Place of Publication:

Cham, Switzerland : Springer, [2022]

Summary:

This book surveys current and future approaches to generating video game content with machine learning or Procedural Content Generation via Machine Learning (PCGML). Machine learning is having a major impact on many industries, including the video game industry. PCGML addresses the use of computers to generate new types of content for video.

Contents:

Intro

Preface

Acknowledgments

Contents

About the Authors

1 Introduction

[DELETE]

1.1 Procedural Content Generation

1.2 Machine Learning

1.3 History of PCGML

1.4 Who is this Book For?

1.5 Who is this Book Not For?

1.6 Book Outline

2 Classical PCG

2.1 What is Content?

2.2 Constructive Approaches

2.2.1 Noise

2.2.2 Rules

2.2.3 Grammars

2.3 Constraint-Based Approaches

2.4 Search-Based Approaches

2.4.1 Evolutionary PCG

2.4.2 Quality-Diversity PCG

2.5 Takeaways

3 An Introduction of ML Through PCG

3.1 Data and Hypothesis Space

3.2 Loss Criterion

3.3 Underfitting and Overfitting/Variance and Bias

3.4 Takeaways

4 PCGML Process Overview

4.1 Produce or Acquire Training Data

4.1.1 Existing Training Data

4.1.2 Producing Training Data

4.2 Train the Model

4.2.1 Output Size

4.2.2 Representation Complexity

4.2.3 Train, Validation, and Test Splits

4.3 Generate Content

4.3.1 Exploration vs. Exploitation in Generation

4.3.2 Postprocessing

4.4 Evaluate the Output

4.5 Takeaways

5 Constraint-Based PCGML Approaches

5.1 Learning Platformer Level Constraints

5.2 Learning Quest Constraints

5.3 WaveFunctionCollapse

5.3.1 Extract

5.3.2 Observe

5.3.3 Propagate

5.3.4 Extending WaveFunctionCollapse

5.4 Takeaways

6 Probabilistic PCGML Approaches

6.1 What are Probabilities?

6.1.1 Learning Platformer Level Probabilities

6.2 What are Conditional Probabilities?

6.2.1 Learning Platformer Level Conditional Probabilities

6.3 Markov Models

6.3.1 Markov Chains

6.3.2 Multi-dimensional Markov Chains

6.3.3 Markov Random Fields

6.3.4 Other Markov Models

6.4 Bayesian Networks

6.5 Latent Variables

6.5.1 Clustering

6.6 Takeaways.

7 Neural Networks-Introduction

7.1 Stochastic Gradient Descent

7.2 Activation Functions

7.3 Artificial Neural Networks

7.4 Case Study: NN 2D Markov Chain

7.5 Case Study: NN 1D Regression Markov Chain

7.6 Case Study: NN 2D AutoEncoder

7.7 Takeaways

8 Sequence-Based DNN PCGML

8.1 Recurrent Neural Networks

8.2 Gated Recurrent Unit and Long Short-Term Memory RNNs

8.2.1 Long Short-Term Memory RNNs

8.3 Sequence-Based Case Study-Card Generation

8.4 Sequence-to-Sequence Recurrent Neural Networks

8.5 Transformer Models

8.5.1 Case Study-Sequence to Sequence Transformer for Card Generation

8.6 Practical Considerations

8.7 Takeaways

9 Grid-Based DNN PCGML

9.1 Convolutions

9.2 Padding and Stride Behavior

9.3 Generative Adversarial Networks

9.4 Practical Considerations

9.5 Case Study-CNN Variational Autoencoder for Level Generation

9.6 Case Study-GANs for Sprite Generation

9.7 Takeaways

10 Reinforcement Learning PCG

10.1 One-Armed Bandits

10.2 Pixel Art Example

10.3 Markov Decision Process (MDP)

10.4 MDP Example

10.5 Tabular Q-Learning

10.5.1 Rollout Example

10.5.2 Q-Update

10.5.3 Q-Update Example

10.5.4 Rollout Example 2

10.5.5 Tabular Q-learning Wrap-up

10.6 Deep Q-Learning

10.7 Application Examples

10.8 Takeaways

11 Mixed-Initiative PCGML

11.1 Existing PCG Tools in the Wild

11.1.1 Classical PCG Tools

11.1.2 Microsoft FlightSim

11.1.3 Puzzle-Maker

11.2 Structuring the Interaction

11.2.1 Integrating with the PCGML Pipeline

11.2.2 Understanding the Model

11.2.3 Understanding the User

11.3 Design Axes

11.3.1 AI vs. User Autonomy

11.3.2 Static vs. Dynamic Model Systems

11.4 Takeaways

12 Open Problems

12.1 Identifying Open Problems

12.2 Problem Formulation.

12.2.1 Underexplored Content Types

12.2.2 Novel Content Generation

12.2.3 Controllability

12.3 Input

12.3.1 Data Sources

12.3.2 Representations

12.3.3 Data Augmentation

12.4 Models and Training

12.5 Output

12.5.1 Applications

12.5.2 Evaluation

12.6 Discussion

13 Resources and Conclusions

13.1 PCGML Resources

13.1.1 Other Textbooks

13.1.2 Code Repositories

13.1.3 Libraries

13.1.4 Datasets

13.1.5 Competitions and Jams

13.1.6 Venues

13.1.7 Social Media

13.2 Conclusions

References.

Notes:

Includes bibliographical references.

Description based on print version record.

Description based on publisher supplied metadata and other sources.

ISBN:

9783031167195

3031167198

OCLC:

1354208505