Adaptive Games Content Generation

“Mario”

Mohammad ShakerDepartment of Artificial Intelligence

IT University of DamascusSeminar of Artificial Neural Networks

ZGTR

Outline

• Readings

• Motivation

• The proposed approach

• Experiments

• ANN Implementation

• Results

• Conclusion

Readings• Towards Automatic Personalized Content

Generation for Platform Games Noor Shaker, Georgios N. Yannakakis, Member, IEEE, and Julian Togelius,

Member, IEEE

• Feature Analysis for Modeling Game Content

Quality Noor Shaker, Georgios N. Yannakakis, Member, IEEE, and Julian Togelius,

Member, IEEE

Motivation

Motivation

Motivation

Motivation

Motivation

Motivation

Motivation

The Big Picture

The Big Picture

Game Player

The Big Picture

Game Player

The Big Picture

Game Player

Player ExperienceModel

The Big Picture

Game Player

Player ExperienceModel

Game Adaptation

The Big Picture

Game Player

Player ExperienceModel

Game Adaptation

The Big Picture

Game Player

Player ExperienceModel

Game Adaptation

The Big Picture

Game Player

Player ExperienceModel

Game Adaptation

The Game

The Game

Open Questions! Session period? (frequency of adaptation)

The most useful information about game content?

Game aspects with major affect on player

experience?

Open Questions! Session period? (frequency of adaptation)

The most useful information about game content?

Game aspects with major affect on player

experience?

Approach

Design

Approach

Design CollectData

Approach

Design CollectData

ModelPlayer’s Emotion

Data Collection 40 small levels

(one-third of usual size)

600 game pairs

Features Six controllable features

Players preferences of engagement

Data Collection 40 small levels

(one-third of usual size)

600 game pairs

Features Six controllable features

Players preferences of engagement

Data Collection - Controllable Features

number of gaps

average width of gaps

number of enemies

number of powerups

number of boxes

Enemies placement Around horizontal boxes

Around gaps

Random placement

Experiments

Experiment 1 How long the game session should be in order to be

able to extract useful information?

Experiment 1 How long the game session should be in order to be

able to extract useful information?

Segmentation

Segmentation

Content-Driven Preference Learning

• It’s the use of genetic algorithms to evolve the

weight of neural networks to learn preference data.

Content-Driven Preference Learning

Levels

• It’s the use of genetic algorithms to evolve the

weight of neural networks to learn preference data.

Content-Driven Preference Learning

Levels Segmentation

• It’s the use of genetic algorithms to evolve the

weight of neural networks to learn preference data.

Content-Driven Preference Learning

LevelsFeature

extractionSegmentation

• It’s the use of genetic algorithms to evolve the

weight of neural networks to learn preference data.

Content-Driven Preference Learning

NeuroEvolutionarypreference

learningLevels

Feature extraction

Segmentation

• It’s the use of genetic algorithms to evolve the

weight of neural networks to learn preference data.

Content-Driven Preference Learning

NeuroEvolutionarypreference

learningLevels

Player’sEngagement

Feature extraction

Segmentation

• It’s the use of genetic algorithms to evolve the

weight of neural networks to learn preference data.

NeuroEvolutionarypreference

learning

Feature extraction

Content-Driven Preference Learning

Feature extraction

Feature extraction

Feature extraction

NeuroEvolutionarypreference

learning

NeuroEvolutionarypreference

learning

NeuroEvolutionarypreference

learning

Experiment 2 How can we extract the most useful information

about game content?

Experiment 2 How can we extract the most useful information

about game content?

Game Content Representation

Statistical features

Sequences

Game Content Representation

Statistical features Six controllable features

Used for level generation

Sequences

Game Content Representation

Statistical features Six controllable features

Used for level generation

Sequences Numbers representing different types of game content

o Platform structure, S

o Enemies placement, Ep

o Enemies and items placement, D

Sequence Mining

Sequence Mining

Sequence Mining-SPADE

SPADE

Frequent

Subseq.

occurrences

40 levels seq.

Content-Driven Preference Learning

ANN-

NeuroEvolutionary

Preference

Learning

Statisticalfeatures

Player’s Engagement

ANN-

NeuroEvolutionary

Preference

Learning

Sequentialfeatures

Player’s Engagement

Experiment 3 What are the game aspects that have the major

affect on player experience?

Experiment 3 What are the game aspects that have the major

affect on player experience?

Content-Driven Preference Learning

ANN-

NeuroEvolutionary

Preference

Learning

Sequentialfeatures

Player’s Engagement

Statisticalfeatures

Featureselection

ANN Implementation

ANN Implementation• Multilayer perceptrons (MLPs)

o ANN inputs

• Controllable features

• Sequences as features

o ANN output

• Value of the engagement preference

ANN Training• Genetic algorithms (GAs)

o No prescribed target outputs

ANN Training• Genetic algorithms (GAs)

o No prescribed target outputs

• How it works?

ANN Training• Genetic algorithms (GAs)

o No prescribed target outputs

• How it works?

players’ reported

emotional preferences

magnitude of corresponding model (ANN)

output

ANN Training• Genetic algorithms (GAs)

o No prescribed target outputs

• How it works?

players’ reported

emotional preferences

magnitude of corresponding model (ANN)

output-

ANN Implementation

ANN Implementation

SF

CF

ANN Implementation

SF

CF

Optimizing Neural Networks Topologies

• 2 hidden layers (Max.)

Optimizing Neural Networks Topologies

• 2 hidden layers (Max.)

• Multiple experiments 1 hidden layer, Adding two neurons at each step

2 neurons - 8 neurons

Optimizing Neural Networks Topologies

• 2 hidden layers (Max.)

• Multiple experiments 1 hidden layer, Adding two neurons at each step

2 neurons - 8 neurons

2 hidden layers, Adding two neurons at each step

1st Hidden layer

2 neurons - 10 neurons

2nd Hidden layer

2 neurons - 8 neurons

Optimizing Neural Networks Topologies

• 2 hidden layers (Max.)

• Multiple experiments 1 hidden layer, Adding two neurons at each step

2 neurons - 8 neurons

2 hidden layers, Adding two neurons at each step

1st Hidden layer

2 neurons - 10 neurons

2nd Hidden layer

2 neurons - 8 neurons

ANN Adaptation

ANN Implementation

SF

CF

ANN Adaptation

SF

CF

Prediction ofplayer’s emotion

ANN Adaptation

SF

CF

Prediction ofplayer’s emotion

Gaps #: 4-10Gaps width: 10-30Gaps placement: 0-1Switch:0-1

ANN Adaptation

SF

CF

Prediction ofplayer’s emotion

Exhaustive search

Gaps #: 4-10Gaps width: 10-30Gaps placement: 0-1Switch:0-1

ANN Adaptation

SF

CF

Prediction ofplayer’s emotion

Exhaustive search

Gaps #: 4-10Gaps width: 10-30Gaps placement: 0-1Switch:0-1

ANN Adaptation

level1 level2

Adapt

level20

Adapt Adapt

level21 level50

Adapt

ANN-

NeuroEvolutionary

Preference

Learning

Sequentialfeatures

Player’s Engagement

Statisticalfeatures

Featureselection

Neural Networks Input Representation

Game Content Representation

Sequentialfeatures

Statisticalfeatures

Featureselection

Game Content Representation

Game Content Representation

The best-performing MLP models evaluated on occurrences

of frequent subsequences of length three extracted from the 40 levels

The topology and performance of the best MLP models evaluated on full and

partial information about game content. the MLP performance presented is the

average performance over 20 runs.

MLPs Performance on Full Information about Game Content

Results

The performance and topologies of MLP models evaluated on full and partial

information of game content using statistics from the game window and from two

and three segments to which the window has been divided. The performance

presented is the average over five runs.

Content-Driven Preference Learning

ANN-

NeuroEvolutionary

Preference

Learning

Sequentialfeatures

Player’s Engagement

Statisticalfeatures

Featureselection

Conclusion

Combining both sequential and statistical features

gives better results in predicting players' reported

emotional state.

Partitioning the level causes a significant decrease

(p < 0.05) in the accuracy of predicting player’s reported engagement. This suggests that there

might be information loss because of decomposing

the data and that this loss causes a performance

decrease.

Multiple perspectives can be done in reference to

this study which is already going on!

Thank you!