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A presentation for fourth year project in Information Technology Engineering, Artificial Intelligence Department, Damascus, Syria. This research study take games development concept to a new level, especially the so called First Person Shooter (FPS) Games This study outline the three basic models: Level Design and Procedural Content Generation for FPS games, Preference Learning and Adaptive Content Generation. Each implemented and integrated with CUBE opensource game engine. You can find the publications conducted by this research study here: http://mohammadshakergtr.wordpress.com/publication/
Citation preview
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
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
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
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
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 2005$29B worldwide business
In 2008surpassed music industry
Statistics
In 2005$29B worldwide business
In 2008surpassed music industry
In 2010$42B worldwide business
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
Previous Contributions
Resident Evil
Previous Contributions
Resident Evil
Previous Contributions
Previous Contributions
Silent Hill
Previous Contributions
Splinter
Cell
Double
Agent
Previous Contributions
Splinter
Cell
Double
Agent
Previous Contributions
Mario
Game
The Big Picture
Game Player
Player ExperienceModel
Adaptation Model
The Big Picture
Game Player
Player ExperienceModel
Adaptation Model
The Big Picture
Game Player
Player ExperienceModel
Adaptation Model
The Big Picture
Game Player
Adaptation Model
Player ExperienceModel
Approach
Levels
Design
Levels
Design
Levels
Design
Preference
Learning
Model
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
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
All Black
Levels Generation
Levels Generation
Levels Generation
Levels Generation
Levels Generation
Levels Generation
Levels Generation
Levels Generation
Levels Generation
Levels Generation
Levels Generation
Levels Generation
Levels Generation
Black and White
Optimizing the algorithm
Black and white grouped
Optimizing the algorithm
Black and white grouped
PCGWaypoints Creation
Waypoints Creation
Waypoints
Waypoints Creation
Waypoints Creation
Waypoints Creation
Waypoints Creation
Waypoints Creation
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
Items Placement
Items Placement
Items Placement
Items Placement
Items Placement
Items Placement
Items Placement, ConstraintsConstraints
Items Placement, ConstraintsConstraints
FPSFeatures
Gameplay
Features
Controllable
Features
Controllable Features
• Enemies Count
• Difficulty
• Weapons Type Percentage
• Level Type
• Ammo Distribution Percentage
Controllable Features
• Difficulty
Controllable Features
• Weapons Type Percentageo Explosive
o Bullets-Based
• 𝐸𝑥𝑝𝑜𝑠𝑖𝑣𝑒 𝑊𝑒𝑎𝑝𝑜𝑛𝑠% = [0,1]
• 𝐵𝑢𝑙𝑙𝑒𝑡𝑠 𝐵𝑎𝑠𝑒𝑑 𝑊𝑒𝑎𝑝𝑜𝑛𝑠 % = 1 − 𝐸𝑥𝑝𝑙𝑜𝑠𝑖𝑣𝑒 𝑊𝑒𝑎𝑝𝑜𝑛𝑠%
Controllable Features
• Level Type
Layered Level Ceiled Arena Lava Arena
Layered Level
Ceiled Arena
Lava Arena
Controllable Features
• Ammo Distribution Percentage
Controllable Features
• Ammo Distribution Percentage
2/18 = 1/9
Controllable Features
• Ammo Distribution Percentage
2/18 = 1/9
Controllable Features
• Ammo Distribution Percentage
2/18 = 1/9
Controllable Features
• Ammo Distribution Percentage
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
• Game pair
Data Collection - Game pair Approach
• 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
Preference Learning Model
Prediction of
player’s
emotion
Preference Learning Model
Prediction of
player’s
emotion?
• SFS o Sequential Forward Selection
• Min Nr. of features
Feature Selection
Preference Learning Model
Controllable features
Gameplay features
Prediction of
player’s
emotion
Preference Learning Model
Controllable features
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
o Computationally affordable
• MLPo Non-linear
o More expressive
o Computationally expensive
o Better performance
SLP VS. MLP
• SLPo Linear
o Expressive
o Computationally affordable
• MLPo Non-linear
o More expressive
o Computationally expensive
o Better performance
• 2-phase approach
2-Phase Approach, Phase #1
Phase-1: Feature Selection
SLP
2-Phase Approach, Phase #1
Phase-1: Feature Selection
SLPAccuracy
(3-fold CV)
SFS features selection
2-Phase Approach, Phase #1
Phase-1: Feature Selection
SLPAccuracy
(3-fold CV)
SFS features selection
• Fast and effective
2-Phase Approach, Phase #1
Phase-1: Feature Selection
SLPAccuracy
(3-fold CV)
SFS features selection
• Fast and effective
• XOR-like relations
CAN’T be detected
Feature Selection - SLP
Features selection with SLP - Challenge
2-Phase Approach, Phase #2
Phase-1: Feature Selection Phase-2: MLP Topology Optimization
SLPAccuracy
(3-fold CV)
SFS features selection
Selected Feature
Subset
2-Phase Approach, Phase #2
Phase-1: Feature Selection Phase-2: MLP Topology Optimization
SLPAccuracy
(3-fold CV)
SFS features selection
MLP
Selected Feature
Subset
2-Phase Approach, Phase #2
Phase-1: Feature Selection Phase-2: MLP Topology Optimization
SLPAccuracy
(3-fold CV)
SFS features selection
MLP
Selected Feature
Subset
MLP topology with
best accuracy
(3-fold CV)
2-Phase Approach, Phase #2
Phase-2: MLP Topology Optimization
MLP
Selected Feature
Subset
MLP topology with
best accuracy
(3-fold CV)
• 2-2 MLP
2-Phase Approach, Phase #2
Phase-2: MLP Topology Optimization
MLP
Selected Feature
Subset
MLP topology with
best accuracy
(3-fold CV)
• 2-2 MLP
• More time VS phase-1
2-Phase Approach, Phase #2
Phase-2: MLP Topology Optimization
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
Preference Learning Model
• 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 2 hidden layers (Max.)
o Multiple experiments
Optimizing ANN Topology - MLP
o 2 hidden layers (Max.)
o Multiple experiments
1 hidden layer, Adding two neurons at each step
2 hidden layers, Adding two neurons at each step
Optimizing ANN Topology - MLP
o 2 hidden layers (Max.)
o Multiple experiments
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
2 neurons - 10 neurons
2nd Hidden layer
2 neurons - 10 neurons
Optimizing ANN Topology - MLP
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
Controllable features
Gameplay features
Prediction ofplayer’s emotion
ANN Adaptation, the model
Controllable features
Gameplay features
Prediction ofplayer’s emotion
Enforcing ALL Controllable features
Gameplay features
Prediction ofplayer’s emotion
ANN Adaptation, the model
Enforcing Controllable Features
MLP Topology
MLP
Selected Feature
Subset
Enforcing Controllable Features
MLP Topology
MLP
Selected Feature
Subset
Remaining
controllable features
Enforcing Controllable Features
MLP Topology
MLP
Selected Feature
Subset
MLP topology with
best accuracy
(3-fold CV)
Remaining
controllable features
Optimizing Topology
Optimizing Topology
MLP Topology
MLP
Selected Feature
Subset
MLP topology with
best accuracy
(3-fold CV)
Remaining
controllable features
Optimizing Topology
MLP Topology Optimization
MLP
Selected Feature
Subset
MLP topology with
best accuracy
(3-fold CV)
Remaining
controllable features
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
Performance over various ANN configurations - Frustration
Pref. Model Adapt. Model
Performance over various ANN configurations - Frustration
The Adaptation
Process
ANN Adaptation, Up and running
Enforced
Controllable features
Gameplay features
Prediction ofplayer’s emotion
ANN Adaptation, Up and running
Enforced
Controllable features
Gameplay features
Prediction ofplayer’s emotion
Exhaustive
search
Enforced
Controllable features
Gameplay features
Prediction ofplayer’s emotion
Exhaustive
search
ANN Adaptation, Up and running
Adaptation, 3-Phase Approach
Phase-1: Initial Gameplay Phase-2: Adaptation Mode
Adaptation, 3-Phase Approach
Phase-1: Initial Gameplay Phase-2: Adaptation Mode
Adaptation
Model
Engine
Manager
Phase-1: Initial Gameplay Phase-2: Adaptation Mode
Random game Adaptation
Model
Engine
Manager
Extract gameplay features for specified player
Adaptation, 3-Phase Approach
Phase-1: Initial Gameplay Phase-2: Adaptation Mode
Random game Adaptation
Model
Engine
Manager
Extract gameplay features for specified player
Adaptation, 3-Phase Approach
Phase-1: Initial Gameplay Phase-2: Adaptation Mode
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
Adaptation, 3-Phase Approach
Phase-1: Initial Gameplay Phase-2: Adaptation Mode
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
Adaptation, 3-Phase Approach
Phase-3: Continues Gameplay
Engine
Manager
Adaptation, 3-Phase Approach
Phase-3: Continues Gameplay
Engine
Manager
Generate game
with specified
controllable
features
Adaptation, 3-Phase Approach
Phase-3: Continues Gameplay
Engine
Manager
Generate game
with specified
controllable
features
Extract gameplay features for specified player
Adaptation, 3-Phase Approach
Phase-2: Adaptation Mode
Adaptation
Model
Set of controllable
features on a fixed
step
Extract best
controllable
features for next
game
Phase-3: Continues Gameplay
Engine
Manager
Generate game
with specified
controllable
features
Extract gameplay features for specified player
Adaptation, 3-Phase Approach
Phase-2: Adaptation Mode
Adaptation
Model
Set of controllable
features on a fixed
step
Extract best
controllable
features for next
game
Phase-3: Continues Gameplay
Engine
Manager
Generate game
with specified
controllable
features
Extract gameplay features for specified player
Adaptation, 3-Phase Approach
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
Experiments, #2
2 Players Preferences Over Alternate Gameplay In Corresponds to Model Performance
Experiments, #2
2 Players Preferences Over Alternate Gameplay In Corresponds to Model Performance
Experiments, #2
2 Players Preferences Over Alternate Gameplay In Corresponds to Model Performance
Experiments, #2
2 Players Preferences Over Alternate Gameplay In Corresponds to Model Performance
Timeline
Timeline
Timeline2011
Ch
oo
sin
g t
he
do
ma
in,
Re
sea
rch
Se
arc
hin
g a
nd
Re
ad
ing
Timeline
9, M10
2011
L10, 11, 12
Ch
oo
sin
g t
he
do
ma
in,
Re
sea
rch
Se
arc
hin
g a
nd
Re
ad
ing
Timeline
9, M10
2011
L10, 11, 12
Ch
oo
sin
g t
he
do
ma
in,
Re
sea
rch
Se
arc
hin
g a
nd
Re
ad
ing
Timeline
9, M10
2011 2012
L10, 11, 12
Pre
sen
tatio
n, P
ap
er
pu
blish
Cu
be
En
gin
e, Te
stin
g
Ad
ap
tiv
e C
on
ten
t G
en
era
tio
n M
od
el
Pre
fere
nc
e M
od
el
Sy
ste
m in
itia
l d
esi
gn
Da
ta C
olle
ctio
n,
STYX
Ev
en
t
De
sig
nin
g le
ve
ls, P
CG
Cu
be
En
gin
e, D
B
Ch
oo
sin
g t
he
do
ma
in,
Re
sea
rch
Se
arc
hin
g a
nd
Re
ad
ing
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
Facial Expression
Facial Expression
Facial Expression
Facial Expression
Facial Expression
Facial Expression
Facial Expression
Facial Expression
Facial Expression
Facial Expression
Facial Expression
Facial Expression
Facial Expression
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…