Adaptive First Person Shooter Game Content Generation

Research in

Adaptive FPS

Game Content

Generation

STYX Team, Damas. FIT

JAEGER ZGTR HASSNOV POD

2012

“STYX ENGINE”

University of Damascus

Faculty of Information Technology

Department of Artificial Intelligence

2012

STYX Team

Ismaeel Abo-Abdalla

Mohammad Shaker

Hasan Serhan

Mehdi Zengi

Supervised By

Dr. Ammar Joukhadar

Eng. Noor Shaker

Presentation timeline

• Research study

• Questions

• Demo

Content

• Intro

• Modelingo Level Design – PCG

o Preference Learning Model

o Adaptive Content Generation Model

• Statistics

• Future Perspective

Call of duty

Call of Duty MW3

1.5 million people

night #1

Call of Duty MW3

1.5 million people

night #1

6.5 million copies

sold on launch day

Call of Duty MW3

1.5 million people

night #1

6.5 million copies

sold on launch day

$775 million

in first 5 days

Call of Duty MW3

1.5 million people

night #1

6.5 million copies

sold on launch day

$775 million

in first 5 days

$1 billion

in 16 days

Statistics

In 2005$29B worldwide business

Statistics


In 2008surpassed music industry

A step further..

• Defining Problem

• Previous Contributions

• Approach

• Research - Adaptive FPS Game

Content Generationo PCG

o Preference Modeling

o Adaptive Modeling

Defining Problem ?

Previous Contributions

Rogue,

early 80s


Resident Evil


Silent Hill


Splinter

Cell

Double

Agent


Mario

Game

The Big Picture

Game Player

Player ExperienceModel

Adaptation Model

The Big Picture

Game Player

Adaptation Model

Player ExperienceModel

Approach

Levels

Design

Levels

Design

Preference

Learning

Model

Levels

Design

Preference

Learning

Model

Adaptive

Content

Generation

Model

Levels

Design

Preference

Learning

Model

Adaptive

Content

Generation

Model

level1 level2

Adapt

level20

Adapt Adapt

level21 levelN

Adapt

Levels Design PCG

PCGProcedural Content Generation

PCGProcedural Content Generation

Randomly Generated Content

Playable Content

PCGImplementing

Levels Generation

Waypoints Creation

Items Placement

PCGLevels Generation

STYX TRAILER Video

Levels Generation

All Black

Levels Generation

Levels Generation

Black and White

Optimizing the algorithm

Black and white grouped

PCGWaypoints Creation

Waypoints Creation

Waypoints

Waypoints Creation

PCGItems Placement

Items Placement Approach

• “SOM” like

Items Placement

𝐼𝑛𝑓𝑙𝑢𝑒𝑛𝑐𝑒 = 𝑒−𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒

𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟𝑖𝑛𝑔 𝑟𝑎𝑑𝑖𝑢𝑠

Items Placement

𝑁𝑒𝑤 𝑉𝑎𝑙𝑢𝑒 = 𝑂𝑙𝑑𝑉𝑎𝑙𝑢𝑒 + 𝐼𝑛𝑓𝑙𝑢𝑒𝑛𝑐𝑒 ∗ (𝐶1 𝑉𝑎𝑙𝑢𝑒 − 𝑂𝑙𝑑𝑉𝑎𝑙𝑢𝑒)

Items Placement

Items Placement, ConstraintsConstraints

FPSFeatures

Gameplay

Features

Controllable

Features

Controllable Features

• Enemies Count

• Difficulty

• Weapons Type Percentage

• Level Type

• Ammo Distribution Percentage


• Difficulty


• Weapons Type Percentageo Explosive

o Bullets-Based

• 𝐸𝑥𝑝𝑜𝑠𝑖𝑣𝑒 𝑊𝑒𝑎𝑝𝑜𝑛𝑠% = [0,1]

• 𝐵𝑢𝑙𝑙𝑒𝑡𝑠 𝐵𝑎𝑠𝑒𝑑 𝑊𝑒𝑎𝑝𝑜𝑛𝑠 % = 1 − 𝐸𝑥𝑝𝑙𝑜𝑠𝑖𝑣𝑒 𝑊𝑒𝑎𝑝𝑜𝑛𝑠%


• Level Type

Layered Level Ceiled Arena Lava Arena

Layered Level

Ceiled Arena

Lava Arena



2/18 = 1/9

Gameplay

Features

Controllable

Features

Gameplay Features

Shooting

Accuracy

Gameplay Features

Shooting

Accuracy

Shooting Count

Enemy Hits

Gameplay Features• Percentage %

o Weapons Type Usage (Explosive)

o Weapons Type Carrying

• Timeo Bullets-Based Shootingo Explosive Shootingo Per Layero Steadyo Lifetime average

• Counto Jumpingo Jump-Pado Deathso Suicideso Killing(Score)o Hits Taken

• Collectedo Extra Bulletso Healtho Armor

… etc.

Data Collection

STYXEVENT

STYX Event, Facebook

Emotional States

• Game pair

Data Collection - Game pair Approach

• Alternative Forced Choice (4-AFS)o Reported player experience and experimental protocol

Data Collection, 4-AFS Protocol

• Alternative Forced Choice (4-AFS)o Reported player experience and experimental protocol

• How it works?o A [B] > B [A] with E

o Equally E

o Neither E

Data Collection, 4-AFS Protocol

Data Collection

• 4 controllable featureso 24 = 16

• Nr. of pairso 𝐶 16,2 = 120

Data Collection

• Event at Damas. FIT, March 27 and 29

• 120+ game pair over 115 players

• Signed players rights

Data Collection

• 115 players over two days

Males

88%

Females

12%

Gender

Data Collection

• For each player, for each pairo Controllable features

o Gameplay features

o 150 ms logging

o Cam. recording

o Preferred game for each emotion E (using 4-AFS protocol)

Data Collection

Hit and Fail Shots

Games

Total Shots

Levels

Design

Preference

Learning

Model

Adaptive

Content

Generation

Model

level1 level2

Adapt

level20

Adapt Adapt

level21 levelN

Adapt

Modeling

Adaptive

Content

Generation

Model

Preference

Learning

Model

Preference Learning Model

DesignCollect

Data

Model

Player’s

Emotion

• Non-Linear function

Mapping features to

preferenced data

• Non-Linear function

• Noisy nature of the

self-reported data

Mapping features to

preferenced data


Prediction of

player’s

emotion


Prediction of

player’s

emotion?

• SFS o Sequential Forward Selection

• Min Nr. of features

Feature Selection


Controllable features

Gameplay features

Prediction of

player’s

emotion



Gameplay features

Prediction of

player’s

emotion

Game Pair (A, B) features

SLP VS. MLP

• SLP

• MLP

SLP VS. MLP

• SLPo Linear

• MLPo Non-linear

SLP VS. MLP

• SLPo Linear

o Expressive

• MLPo Non-linear

o More expressive

SLP VS. MLP

• SLPo Linear

o Expressive

o Computationally affordable

• MLPo Non-linear

o More expressive

o Computationally expensive

SLP VS. MLP

• SLPo Linear

o Expressive


• MLPo Non-linear

o More expressive


o Better performance

SLP VS. MLP

• SLPo Linear

o Expressive


• MLPo Non-linear

o More expressive


o Better performance

• 2-phase approach

2-Phase Approach, Phase #1

Phase-1: Feature Selection

SLP



SLPAccuracy

(3-fold CV)

SFS features selection



SLPAccuracy

(3-fold CV)


• Fast and effective



SLPAccuracy

(3-fold CV)


• Fast and effective

• XOR-like relations

CAN’T be detected

Feature Selection - SLP

Features selection with SLP - Challenge


Phase-1: Feature Selection Phase-2: MLP Topology Optimization

SLPAccuracy

(3-fold CV)


Selected Feature

Subset



SLPAccuracy

(3-fold CV)


MLP

Selected Feature

Subset



SLPAccuracy

(3-fold CV)


MLP

Selected Feature

Subset

MLP topology with

best accuracy

(3-fold CV)


Phase-2: MLP Topology Optimization

MLP

Selected Feature

Subset

MLP topology with

best accuracy

(3-fold CV)

• 2-2 MLP



MLP

Selected Feature

Subset

MLP topology with

best accuracy

(3-fold CV)

• 2-2 MLP

• More time VS phase-1



MLP

Selected Feature

Subset

MLP topology with

best accuracy

(3-fold CV)

• 2-2 MLP

• More time VS phase-1

• Higher prediction

accuracy

Feature Selection - MLP

Features selection with 2_2 MLP - Challenge


• Genetic algorithms (GAs)

Player

reported

emotional

preferences

Magnitude of

corresponding

model (ANN)

output-

3-fold Cross Validation

o 2 hidden layers (Max.)

Optimizing ANN Topology - MLP


o Multiple experiments




1 hidden layer, Adding two neurons at each step

2 hidden layers, Adding two neurons at each step




1 hidden layer, Adding two neurons at each step

2 neurons - 10 neurons

2 hidden layers, Adding two neurons at each step

1st Hidden layer


2nd Hidden layer



Performance VS Runs

1 hidden, 10 neurons topology performance over 50 runs - Challenge

Optimizing Topology, Pref. Model

Performance over various ANN configurations - Challenge

Levels

Design

Preference

Learning

Model

Adaptive

Content

Generation

Model

level1 level2

Adapt

level20

Adapt Adapt

level21 levelN

Adapt

Adaptive

Content

Generation

Model

Preference

Learning

Model

Adaptive Content

Generation Model

ANN Preference Model


Gameplay features

Prediction ofplayer’s emotion

ANN Adaptation, the model


Gameplay features


Enforcing ALL Controllable features

Gameplay features


ANN Adaptation, the model

Enforcing Controllable Features

MLP Topology

MLP

Selected Feature

Subset


MLP Topology

MLP

Selected Feature

Subset

Remaining

controllable features


MLP Topology

MLP

Selected Feature

Subset

MLP topology with

best accuracy

(3-fold CV)

Remaining


Optimizing Topology

Optimizing Topology

MLP Topology

MLP

Selected Feature

Subset

MLP topology with

best accuracy

(3-fold CV)

Remaining


Optimizing Topology

MLP Topology Optimization

MLP

Selected Feature

Subset

MLP topology with

best accuracy

(3-fold CV)

Remaining


Enforcing gives

the designer all

the flexibilitythe parameter

space offers

Enforcing

dropsperformance

Optimizing Topology, Pref. Model

Performance over various ANN configurations - Frustration

Optimizing Topology, Adapt. Model


Pref. Model Adapt. Model


The Adaptation

Process

ANN Adaptation, Up and running

Enforced


Gameplay features



Enforced


Gameplay features


Exhaustive

search

Adaptation, 3-Phase Approach

Phase-1: Initial Gameplay Phase-2: Adaptation Mode



Adaptation

Model

Engine

Manager


Random game Adaptation

Model

Engine

Manager

Extract gameplay features for specified player



Random game Adaptation

Model

Set of controllable

features on a fixed

step

Engine

Manager

Extract best

controllable

features for next

gameExtract gameplay features for specified player


Phase-3: Continues Gameplay

Engine

Manager



Engine

Manager

Generate game

with specified

controllable

features


Phase-2: Adaptation Mode

Adaptation

Model

Set of controllable

features on a fixed

step

Extract best

controllable

features for next

game


Engine

Manager

Generate game

with specified

controllable

features



ANN Adaptation

level1 level2

Adapt

level20

Adapt Adapt

level21 levelN

Adapt

Statistical Experiments

Adaptation Model Performance VS Reported Player Pref.

Experiments, #1

Experiments, #1

Adaptation Model Performance VS Reported Player Pref.

Experiments, #2

2 Players Preferences Over Alternate Gameplay In Corresponds to Model Performance

Timeline

Timeline2011

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Timeline

9, M10

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L10, 11, 12

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Timeline

9, M10

2011 2012

L10, 11, 12

Pre

sen

tatio

n, P

ap

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STYX

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CG

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Timeline

9, M10

2011 2012

L10, 11, 12 L1, E2 L3M2, M3 4 5 M6, 7RIGHT

NOW!

Presentation,

Paper

publish

Cube Engine, Testing

Adaptive Content

Generation Model

Preference Model

System initial

design

Data

Collection

, STYX

Event

Designing levels,

PCG

Cube Engine,

Database

Timeline - Implementing

2012

L1, E2 L3M2, M3 4 5 M6, 7RIGHT

NOW!

Engine

Future Perspectives

More

complicated, robust

models design

Addition emotional states

Music manipulation

More controllable features

More gameplay features

More Players

• Deeper Statistical Understanding

• More robust models design

• Logging almost EVERYTHING, 150 ms

(Move, status, etc.)

More Players

• Deeper Statistical Understanding

• More robust models design

• Logging almost EVERYTHING, 150 ms

(Move, status, etc.)

• Facial expression modeling

Facial Expression

Levels Design

Levels Design

Sniper Position

Levels Design

Gallery, Halo 3

Levels Design

Strong Hold, COD MW3

Extending

Neuro-Evolutionary

Preference Learning

through

Player Modeling

References

• Many! (find out more in the doc.)

• Special thanks to:o Noor Shaker

o Georgios N. Yannakakis

o Julian Togelius

o Luigi Cardamone

team’s out,

Thanks for listening…

Adaptive First Person Shooter Game Content Generation

Technology

Transcript of Adaptive First Person Shooter Game Content Generation