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TRANSCRIPT
Chapter 1: Introduction
Playing Games
(Chapter 3)
Generating Content
(Chapter 4)
ModelingPlayers
(Chapter 5)Game AI Panorama
(Chapter 6)
AI Methods(Chapter 2)
Frontier Game AI Research
(Chapter 7)
Chapter 2: AI Methods
Seek for pellets
Chase Ghosts
Evade Ghosts
Ghost on sight
No visible ghost
Power pill eaten
Ghosts flashing
Move(Priority)
Ghost free corridor
Corridor with pellets
Corridor without pellets
Seek for Pellets
Pellet Found Eat next pellet
Until ghost on sight
Spot EnemySelect Weapon
(Probability)
Mini Gun PistolRocket
Launcher
Attack Enemy
Aim Shoot!
Until Health = 0
0.5 0.30.2
10 -1 5 0 2 3 -1
5
0
0
5 MinMax
10 5 3 0
0 0 1 1 1 0 1 0 0 1 00 0 11 1 0 1 00 1 0
0 0 1 1 1 0 1 0 0 1 00 0 0 1 1 0 1 1 0 1 0
0 0 1 1 1 0 1 0 0 1 00 0 11 1 0 1 00 1 0
0 0 1 1 1 0 1 0 0 1 00 0 0 1 0 1 1 1 0 1 0
0 0 1 1 1 0 1 0 0 1 0
0 1 0 1 0 1 1 0 0 0 10 1 0 1 0 1 1 0 0 0 1
0 0 1 1 1 0 1 0 0 1 0
p
0 0 1 1 1 0 1 0 0 1 0
0 1 0 1 0 1 1 0 0 0 10 1 0 1 0 1 1 0 0 0 1
0 0 1 1 1 0 1 0 0 1 0
𝑥2
𝑥1
6
1 4
23
5
<20 >28[20-28]
fairlowyesno high
Age?
Economy Employed? Salary?
City CarNo Car Sports Compact SUV
near not visible
fairnearfarnear far
Ghost
Power pill Pellet
Evade ghosts
Aim for pill
Aim for pellet
Aim for pellet
Aim for fruit
𝑥2
𝑥1
w
Neuron𝑥1
𝑤2
𝑤1
𝑤𝑛
𝑏
𝑥2
𝑥𝑛
x ∙ w + 𝑏
𝑔
𝑔(x ∙ w + 𝑏)
Hidden Layer Output LayerInput Layer
𝑥1
𝑥3
𝑥2
𝑤14𝑤48
𝑤49
𝑤37𝑤79
𝑎8
𝑎9
4
1
2
3
5
6
7
9
8
Selection Expansion Simulation Backpropagation
TreePolicy
DefaultPolicy
Environment (e.g. Maze)
State (s) Action (α)Reward (r)
Agent
G
Fitness value 𝑓2
0 0 1 1 1 0𝑤1 𝑤2 𝑤3 𝑤4 𝑤5 𝑤𝑛
0 0 1 1 1 0𝑤1 𝑤2 𝑤3 𝑤4 𝑤5 𝑤𝑛
0 0 1 1 1 0𝑤1 𝑤2 𝑤3 𝑤4 𝑤5 𝑤𝑛
1
2
P
Convolution Pooling
Reward
Action
Chapter 3: Playing Games
Player Non-Player
Win
MotivationGames as AI testbeds, AI that
challenges players, Simulation-based testing
ExamplesBoard Game AI (TD-Gammon, Chinook, Deep Blue, AlphaGo, Libratus), Jeopardy! (Watson),
StarCraft
MotivationPlaying roles that humans would
not (want to) play, Game balancing
ExamplesRubber banding
Experience
MotivationSimulation-based testing,
Game demonstrations
ExamplesGame Turing Tests (2kBot
Prize/Mario),Persona Modelling
MotivationBelievable and human-like agents
ExamplesAI that: acts as an adversary, provides
assistance, is emotively expressive, tells a story, …
BattleshipScrabble
Poker
Super Mario BrosHalo
StarCraft
Ms Pac-Man
LudoMonopoly
Backgammon
Pac-ManAtari 2600
CheckersChess
Go
Deterministic Non-deterministic Stochasticity
Time Granularity
Turn-Based
Real-Time
ObservabilityPe
rfec
t In
form
atio
nIm
per
fect
Info
rmat
ion
Chapter 4: Generating Content
37%
2%
6%3%
40%
12%
Art Manufacturing Other Debugging Marketing Programming
Ro
leM
eth
od
Co
nte
nt
Stochastic Deterministic
Constructive
ControllableNon-Controllable
Optional Content Necessary ContentContent Type
Determinism
Controllability
Iterativity Generate-and-test
Autonomous Mixed-Initiative
Experience-Agnostic Experience-Driven
Autonomy
Experience
Generation 0
Generation 1Midpoint displacement
Generation 2
Generation 3
Final Generation
Initialize Corner Values
Perform Diamond Step
Perform Square Step
Perform Diamond Step
Perform Square Step
Autonomous Mixed-Initiative
Designer (Initiative)
Player (Experience)
Exp
erie
nce
D
rive
nEx
per
ien
ce
Agn
ost
ic
Super Mario Bros (Pedersen et al., 2010)
Sonancia (Lopes et al., 2015)
Sentient Sketchbook (Liapis et al., 2013)
SpeedTree (IDV, 2002)
StarCraft Maps (Togelius et al., 2013)
Garden of Eden Creation Kit (Bethesda, 2009)
Ropossum (Shaker et al., 2013)
Tanagra (Smith et al., 2010)
Chapter 5: Modeling Players
Input OutputComputational (Player) Model
Gameplay
Objective
Context
Player Profile
Model-Based [Top-Down](Psychology, Cognitive Science, Game
Studies, …)
Model Free [Bottom-Up](Data Science, Machine Learning)
Numerical (Interval)
Nominal (Classes)
Ordinal (Ranks)
No Output
Regression
Classification
Preference Learning
Unsupervised Learning (Clustering, Frequent Pattern Mining)
Free Response vs. Forced Response
First Person vs. Third Person
Discrete vs. Continuous
Time-Discrete vs. Time-Continuous
Pre vs. During vs. Post
Valence
Arousal
Pleasant ( + )Unpleasant ( - )
Excitement
Happiness
TirednessBoredom
Sadness
Anger
Frustration
Fear
Relaxation
Activation ( + )
Deactivation ( - )
Boredom
Anxiety
Skills
Ch
alle
nge
FlowChannel
Nearest Monster
Nearest Treasure
Nearest Portion
Exit SafeNearest Treasure
Safe
Nearest Portion
SafeExit
Hit Points
Monster Treasure Portion Exit SafeTreasure
SafePortion
SafeExit
Chapter 6: Game AI Panorama
Model Players (Behavior)
Model
Generate
Content
Behavior
Designer
Player
AI Researcher
Producer / Publisher
Model Players (Behavior, Experience)
Generate Content (Assisted)
Generate Content (Autonomously)Play Games(Win [NPC], Experience [NPC])
Play Games (Win [PC], Experience [PC])
DO (Process) WHAT (Context) FOR WHO (End User) GAME AI AREA
Model Players
Player
Game
Content NPCs
Interactio
nExperience Behavior
Generate Content
Autonomously
Play Games [as NPC]
Win Experience
Model Players
Experience Behavior
Generate Content
AutonomouslyAssisted
Play Games (as PC or NPC)
To WinFor the (Game)
Experience
Model Players
Experience Behavior
Generate Content
AutonomouslyAssisted
Play Games (as PC or NPC)
To WinFor the (Game)
Experience
Model Players
Experience Behavior
Generate Content
AutonomouslyAssisted
Play Games (as PC or NPC)
To WinFor the (Game)
Experience
Model Players
Experience Behavior
Generate Content
AutonomouslyAssisted
Play Games (as PC or NPC)
To WinFor the (Game)
Experience
Model Players
Experience Behavior
Generate Content
AutonomouslyAssisted
Play Games (as PC or NPC)
To WinFor the (Game)
Experience