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Chapter 1: Introduction
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Frontier Game AI Research
Playing Games (Chapter 3) Generating Content (Chapter 4) Modeling Players (Chapter 5) Game AI Panorama (Chapter 6) AI Methods (Chapter 2) Frontier Game AI Research (Chapter 7)
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Chapter 2: AI Methods
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Seek for pellets Chase Ghosts Evade Ghosts Ghost on sight No visible ghost Power pill eaten Ghosts flashing
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Corridor without pellets
Move (Priority) Ghost free corridor Corridor with pellets Corridor without pellets Seek for Pellets Pellet Found Eat next pellet Until ghost on sight
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Spot Enemy Select Weapon (Probability) Mini Gun Pistol Rocket Launcher Attack Enemy Aim Shoot! Until Health = 0 0.5 0.3 0.2
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10 -1 5 2 3 Min Max
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1 1
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1 p 1
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𝑥 2 𝑥 1 6 1 4 2 3 5
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<20 >28 [20-28] fair low yes no high
Age? Economy Employed? Salary? City Car No Car Sports Compact SUV
![<20 >28 [20-28] fair low yes no high](http://slideplayer.com./slide/12981784/79/images/11/%3C20+%3E28+%5B20-28%5D+fair+low+yes+no+high.jpg "Age Economy. Employed Salary City Car. No Car. Sports. Compact. SUV.")
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near not visible fair far
Ghost Power pill Pellet Evade ghosts Aim for pill Aim for pellet Aim for fruit
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𝑥 2 𝑥 1 w∙x+𝑏=1 w∙x+𝑏=0 w∙x+𝑏=−1 w Maximum margin
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Neuron 𝑥 1 𝑤 2 𝑤 1 𝑤 𝑛 𝑏 𝑥 2 𝑥 𝑛 x∙w +𝑏 𝑔 𝑔(x∙w +𝑏)
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𝑥 1 𝑥 2 𝑥 3 Hidden Layer Output Layer Input Layer 𝑤 14 𝑤 48 𝑤 49 𝑤 37
𝑤 79 𝑎 8 𝑎 9 4 1 2 3 5 6 7 9 8
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Selection Expansion Simulation Backpropagation Tree Policy Default
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G Agent State (s) Reward (r) Action (α) Environment (e.g. Maze)
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Fitness value 𝑓 2 1 𝑤 1 𝑤 2 𝑤 3 𝑤 4 𝑤 5 𝑤 𝑛 2 P
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Convolution Pooling Reward Action
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Chapter 3: Playing Games
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Player Non-Player Win Experience Motivation
Games as AI testbeds, AI that challenges players, Simulation-based testing Examples Board Game AI (TD-Gammon, Chinook, Deep Blue, AlphaGo, Libratus), Jeopardy! (Watson), StarCraft Playing roles that humans would not (want to) play, Game balancing Rubber banding Experience Simulation-based testing, Game demonstrations Game Turing Tests (2kBot Prize/Mario),Persona Modelling Believable and human-like agents AI that: acts as an adversary, provides assistance, is emotively expressive, tells a story, …
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Stochasticity Time Granularity Observability
Battleship Scrabble Poker Super Mario Bros Halo StarCraft Ms Pac-Man Ludo Monopoly Backgammon Pac-Man Atari 2600 Checkers Chess Go Deterministic Non-deterministic Stochasticity Time Granularity Turn-Based Real-Time Observability Information Perfect Imperfect
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Chapter 4: Generating Content
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Stochastic Deterministic Constructive Controllable Non-Controllable
Role Method Content Stochastic Deterministic Constructive Controllable Non-Controllable Optional Content Necessary Content Content Type Determinism Controllability Iterativity Generate-and-test Autonomous Mixed-Initiative Experience-Agnostic Experience-Driven Autonomy Experience
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Generation 0 Generation 1 Midpoint displacement Generation 2 Generation 3 Final Generation
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Initialize Corner Values
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Perform Diamond Step
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Perform Square Step
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Perform Diamond Step
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Perform Square Step
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Designer (Initiative)
Autonomous Mixed-Initiative Designer (Initiative) Player (Experience) Experience Driven Experience Agnostic 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)
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Chapter 5: Modeling Players
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Computational (Player) Model
Input Computational (Player) Model Output Gameplay Model-Based [Top-Down] (Psychology, Cognitive Science, Game Studies, …) Free Response vs. Forced Response Objective First Person vs. Third Person Context Player Profile Discrete vs. Continuous Time-Discrete vs. Time-Continuous Numerical (Interval) Model Free [Bottom-Up] (Data Science, Machine Learning) Regression Nominal (Classes) Classification Pre vs. During vs. Post Ordinal (Ranks) Preference Learning No Output Unsupervised Learning (Clustering, Frequent Pattern Mining)
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Arousal Valence Activation ( + ) Unpleasant ( - ) Pleasant ( + )
Excitement Happiness Tiredness Boredom Sadness Anger Frustration Fear Relaxation Activation ( + ) Deactivation ( - )
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Boredom Anxiety Skills Challenge Flow Channel
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Nearest Monster Treasure Portion Exit Safe Safe Exit Hit Points
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Chapter 6: Game AI Panorama
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Model Players (Behavior)
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
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Player Interaction Game Model Players Experience Behavior
Content NPCs Interaction Experience Behavior Generate Content Autonomously Play Games [as NPC] Win
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Play Games (as PC or NPC)
Model Players Experience Behavior Generate Content Autonomously Assisted Play Games (as PC or NPC) To Win For the (Game) Experience
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Play Games (as PC or NPC)
Model Players Experience Behavior Generate Content Autonomously Assisted Play Games (as PC or NPC) To Win For the (Game) Experience
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Play Games (as PC or NPC)
Model Players Experience Behavior Generate Content Autonomously Assisted Play Games (as PC or NPC) To Win For the (Game) Experience
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Play Games (as PC or NPC)
Model Players Experience Behavior Generate Content Autonomously Assisted Play Games (as PC or NPC) To Win For the (Game) Experience
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Play Games (as PC or NPC)
Model Players Experience Behavior Generate Content Autonomously Assisted Play Games (as PC or NPC) To Win For the (Game) Experience
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