Chapter 1: Introduction Modeling Players Generating Content (Chapter

Chapter 1: Introduction Modeling Players Generating Content (Chapter

Chapter 1: Introduction Modeling Players Generating Content (Chapter 5) (Chapter 4) Game AI Panorama (Chapter 6) Frontier Game AI Research (Chapter 7) AI Methods (Chapter 2)

Playing Games (Chapter 3) Chapter 2: AI Methods Power pill eaten Seek for pellets No visible ghost Ghost on sight Chase Ghosts Ghosts flashing

Evade Ghosts Seek for Pellets Until ghost on sight Move (Priority) Ghost free corridor Pellet Found Corridor with pellets Eat next pellet Corridor without pellets

Attack Enemy Until Health = 0 Spot Enemy 0.5 Mini Gun Select Weapon (Probability) 0.2 Pistol Aim 0.3 Rocket Launcher Shoot! Min Max

5 5 0 10 10 5 -1 5 3 0 2

0 3 -1 0 0 0 1 1 1 0 1

0 0 1 0 0 0 1 0 1 1 0

1 0 1 0 1 0 0 0 1 1 1 0

1 0 0 1 0 0 0 0 1 1 0 1

1 0 1 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 1 0 0 1

1 1 0 1 0 0 1 0 0 1 0

1 0 1 1 0 0 0 1 2 5 1 4

3 2 6 1 Age? <20 Employed? no No Car >28 [20-28] Salary? Economy

yes City Car low Compact fair Sports high SUV Ghost not visible near Pellet Power pill

near Aim for pill far Evade ghosts near Aim for pellet fair Aim for pellet far Aim for fruit 1

2 = 0 + x += 1 w x = w x+ w w um im in ax rg M ma 1

1 Neuron 1 2 2 x w + ( x w +) 14

1 2 3 4 49 1 48 5 8 8 6 9

9 2 3 37 Input Layer 79 7 Hidden Layer Output Layer Selection Expansion Tree Policy

Simulation Default Policy Backpropagation G Agent State (s) Reward (r) Environment (e.g. Maze) Action () 01 0 2 13 14 1 15

0 2 01 02 13 14 15 0 P 01 02 13 14 15 0 Fitness value

Reward Convolution Action Pooling Chapter 3: Playing Games Win Experience Player Non-Player Motivation Games as AI testbeds, AI that challenges players, Simulation-based testing

Motivation Playing roles that humans would not (want to) play, Game balancing Examples Examples Board Game AI (TD-Gammon, Chinook, Deep Blue, AlphaGo, Libratus), Jeopardy! (Watson), StarCraft Rubber banding Motivation Simulation-based testing, Game demonstrations Motivation Believable and human-like agents

Examples Examples Game Turing Tests (2kBot Prize/Mario),Persona Modelling AI that: acts as an adversary, provides assistance, is emotively expressive, tells a story, Imperfect Information Perfect Information Observability Pac-Man Atari 2600

Checkers Chess Go Ms Pac-Man Ludo Monopoly Backgammon Time Granularity Super Mario Bros Halo StarCraft Battleship Scrabble Poker Real-Time Turn-Based

Deterministic Non-deterministic Stochasticity Chapter 4: Generating Content 12.00% 37.00% 40.00% 2.00% 6.00% 3.00% Art Debugging Manufacturing Marketing

Other Programming Conte nt Method Content Type Necessary Content Stochastic Determinism Deterministic Non-Controllable Controllability

Controllable Constructive Iterativity Generate-and-test Autonomous Autonomy Mixed-Initiative Experience-Agnostic Experience Experience-Driven Role

Optional Content Generation 0 Midpoint displacement Generation 1 Generation 2 Generation 3 Final Generation Initialize Corner Values Perform Diamond Step Perform Square Step Perform Diamond Step

Perform Square Step Experience Agnostic Experience Driven Player (Experience) Super Mario Bros (Pedersen et al., 2010) Sonancia (Lopes et al., 2015) SpeedTree (IDV, 2002) StarCraft Maps (Togelius et al., 2013) Autonomous

Sentient Sketchbook (Liapis et al., 2013) Garden of Eden Creation Kit (Bethesda, 2009) Ropossum (Shaker et al., 2013) Tanagra (Smith et al., 2010) Mixed-Initiative Designer (Initiative) Chapter 5: Modeling Players Input Computational (Player) Model Output

Gameplay Model-Based [Top-Down] Objective (Psychology, Cognitive Science, Game Studies, ) Free Response vs. Forced Response Context Player Profile First Person vs. Third Person Discrete vs. Continuous Numerical (Interval)

Regression Time-Discrete vs. Time-Continuous Nominal (Classes) Classification Pre vs. During vs. Post Ordinal (Ranks) Preference Learning No Output Unsupervised Learning (Clustering, Frequent Pattern Mining) Model Free [Bottom-Up]

(Data Science, Machine Learning) Activation ( + ) Arousal Fear Excitement Anger Frustration Happiness Valence Pleasant ( + ) Unpleasant ( - ) Sadness Relaxation

Boredom Deactivation ( - ) Tiredness Challenge Anxiety Boredom Skills Flow Channel Nearest Monster Nearest Treasure

Monster Nearest Treasure Safe Treasure Nearest Portion Safe Nearest Portion Treasure Safe Portion Exit

Portion Safe Hit Points Exit Safe Exit Exit Safe Chapter 6: Game AI Panorama DO (Process) WHAT (Context) FOR WHO (End User) GAME AI AREA

Designer Model Content Player AI Researcher Generate Behavior Producer / Publisher Generate Content (Assisted) Model Players (Behavior, Experience) Generate Content (Autonomously) Play Games

(Win [NPC], Experience [NPC]) Play Games (Win [PC], Experience [PC]) Model Players (Behavior) Player Interaction Model Players Experience Behavior Game Generate Content Autonomously Content

NPCs Play Games [as NPC] Win Experience Play Games (as PC or NPC) For the (Game) To Win Experience Model Players Experience Behavior Generate Content Assisted Autonomously

Play Games (as PC or NPC) For the (Game) To Win Experience Model Players Experience Behavior Generate Content Assisted Autonomously Play Games (as PC or NPC) For the (Game) To Win Experience Model Players Experience

Behavior Generate Content Assisted Autonomously Play Games (as PC or NPC) For the (Game) To Win Experience Model Players Experience Behavior Generate Content Assisted Autonomously

Play Games (as PC or NPC) For the (Game) To Win Experience Model Players Experience Behavior Generate Content Assisted Autonomously

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