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Artificial Intelligence for Games [With CDROM]
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Table of Contents

  • About the Author
  • CONTENTS
  • LIST OF FIGURES
  • PREFACE
  • ACKNOWLEDGMENTS
  • About the CD-ROM
  • PART I AI AND GAMES
    • 1 INTRODUCTION
      • 1.1 WHAT IS AI?
        • 1.1.1 Academic AI
        • 1.1.2 Game AI
      • 1.2 MY MODEL OF GAME AI
        • 1.2.1 Movement
        • 1.2.2 Decision Making
        • 1.2.3 Strategy
        • 1.2.4 Infrastructure
        • 1.2.5 Agent-Based AI
        • 1.2.6 In the Book
      • 1.3 ALGORITHMS, DATA STRUCTURES, AND REPRESENTATIONS
        • 1.3.1 Algorithms
        • 1.3.2 Representations
      • 1.4 ON THE CD
        • 1.4.1 Programs
        • 1.4.2 Libraries
      • 1.5 LAYOUT OF THE BOOK
    • 2 GAME AI
      • 2.1 THE COMPLEXITY FALLACY
        • 2.1.1 When Simple Things Look Good
        • 2.1.2 When Complex Things Look Bad
        • 2.1.3 The Perception Window
        • 2.1.4 Changes of Behavior
      • 2.2 THE KIND OF AI IN GAMES
        • 2.2.1 Hacks
        • 2.2.2 Heuristics
        • 2.2.3 Algorithms
      • 2.3 SPEED AND MEMORY
        • 2.3.1 Processor Issues
        • 2.3.2 Memory Concerns
        • 2.3.3 PC Constraints
        • 2.3.4 Console Constraints
      • 2.4 THE AI ENGINE
        • 2.4.1 Structure of an AI Engine
        • 2.4.2 Toolchain Concerns
        • 2.4.3 Putting It All Together
  • PART II TECHNIQUES
    • 3 MOVEMENT
      • 3.1 THE BASICS OF MOVEMENT ALGORITHMS
        • 3.1.1 Two-Dimensional Movement
        • 3.1.2 Statics
        • 3.1.3 Kinematics
      • 3.2 KINEMATIC MOVEMENT ALGORITHMS
        • 3.2.1 Seek
        • 3.2.2 Wandering
        • 3.2.3 On the CD
      • 3.3 STEERING BEHAVIORS
        • 3.3.1 Steering Basics
        • 3.3.2 Variable Matching
        • 3.3.3 Seek and Flee
        • 3.3.4 Arrive
        • 3.3.5 Align
        • 3.3.6 Velocity Matching
        • 3.3.7 Delegated Behaviors
        • 3.3.8 Pursue and Evade
        • 3.3.9 Face
        • 3.3.10 Looking Where You're Going
        • 3.3.11 Wander
        • 3.3.12 Path Following
        • 3.3.13 Separation
        • 3.3.14 Collision Avoidance
        • 3.3.15 Obstacle and Wall Avoidance
        • 3.3.16 Summary
      • 3.4 COMBINING STEERING BEHAVIORS
        • 3.4.1 Blending and Arbitration
        • 3.4.2 Weighted Blending
        • 3.4.3 Priorities
        • 3.4.4 Cooperative Arbitration
        • 3.4.5 Steering Pipeline
      • 3.5 PREDICTING PHYSICS
        • 3.5.1 Aiming and Shooting
        • 3.5.2 Projectile Trajectory
        • 3.5.3 The Firing Solution
        • 3.5.4 Projectiles with Drag
        • 3.5.5 Iterative Targeting
      • 3.6 JUMPING
        • 3.6.1 Jump Points
        • 3.6.2 Landing Pads
        • 3.6.3 Hole Fillers
      • 3.7 COORDINATED MOVEMENT
        • 3.7.1 Fixed Formations
        • 3.7.2 Scalable Formations
        • 3.7.3 Emergent Formations
        • 3.7.4 Two-Level Formation Steering
        • 3.7.5 Implementation
        • 3.7.6 Extending to More than Two Levels
        • 3.7.7 Slot Roles and Better Assignment
        • 3.7.8 Slot Assignment
        • 3.7.9 Dynamic Slots and Plays
        • 3.7.10 Tactical Movement
      • 3.8 MOTOR CONTROL
        • 3.8.1 Output Filtering
        • 3.8.2 Capability-Sensitive Steering
        • 3.8.3 Common Actuation Properties
      • 3.9 MOVEMENT IN THE THIRD DIMENSION
        • 3.9.1 Rotation in Three Dimensions
        • 3.9.2 Converting Steering Behaviors to Three Dimensions
        • 3.9.3 Align
        • 3.9.4 Align to Vector
        • 3.9.5 Face
        • 3.9.6 Look Where You're Going
        • 3.9.7 Wander
        • 3.9.8 Faking Rotation Axes
    • 4 PATHFINDING
      • 4.1 THE PATHFINDING GRAPH
        • 4.1.1 Graphs
        • 4.1.2 Weighted Graphs
        • 4.1.3 Directed Weighted Graphs
        • 4.1.4 Terminology
        • 4.1.5 Representation
      • 4.2 DIJKSTRA
        • 4.2.1 The Problem
        • 4.2.2 The Algorithm
        • 4.2.3 Pseudo-Code
        • 4.2.4 Data Structures and Interfaces
        • 4.2.5 Performance of Dijkstra
        • 4.2.6 Weaknesses
      • 4.3 A*
        • 4.3.1 The Problem
        • 4.3.2 The Algorithm
        • 4.3.3 Pseudo-Code
        • 4.3.4 Data Structures and Interfaces
        • 4.3.5 Implementation Notes
        • 4.3.6 Algorithm Performance
        • 4.3.7 Node Array A*
        • 4.3.8 Choosing a Heuristic
      • 4.4 WORLD REPRESENTATIONS
        • 4.4.1 Tile Graphs
        • 4.4.2 Dirichlet Domains
        • 4.4.3 Points of Visibility
        • 4.4.4 Polygonal Meshes
        • 4.4.5 Non-Translational Problems
        • 4.4.6 Cost Functions
        • 4.4.7 Path Smoothing
      • 4.5 IMPROVING ON A*
      • 4.6 HIERARCHICAL PATHFINDING
        • 4.6.1 The Hierarchical Pathfinding Graph
        • 4.6.2 Pathfinding on the Hierarchical Graph
        • 4.6.3 Hierarchical Pathfinding on Exclusions
        • 4.6.4 Strange Effects of Hierarchies on Pathfinding
        • 4.6.5 Instanced Geometry
      • 4.7 OTHER IDEAS IN PATHFINDING
        • 4.7.1 Open Goal Pathfinding
        • 4.7.2 Dynamic Pathfinding
        • 4.7.3 Other Kinds of Information Reuse
        • 4.7.4 Low Memory Algorithms
        • 4.7.5 Interruptible Pathfinding
        • 4.7.6 Pooling Planners
      • 4.8 CONTINUOUS TIME PATHFINDING
        • 4.8.1 The Problem
        • 4.8.2 The Algorithm
        • 4.8.3 Implementation Notes
        • 4.8.4 Performance
        • 4.8.5 Weaknesses
      • 4.9 MOVEMENT PLANNING
        • 4.9.1 Animations
        • 4.9.2 Movement Planning
        • 4.9.3 Example
        • 4.9.4 Footfalls
    • 5 DECISION MAKING
      • 5.1 OVERVIEW OF DECISION MAKING
      • 5.2 DECISION TREES
        • 5.2.1 The Problem
        • 5.2.2 The Algorithm
        • 5.2.3 Pseudo-Code
        • 5.2.4 On the CD
        • 5.2.5 Knowledge Representation
        • 5.2.6 Implementation Nodes
        • 5.2.7 Performance of Decision Trees
        • 5.2.8 Balancing the Tree
        • 5.2.9 Beyond the Tree
        • 5.2.10 Random Decision Trees
      • 5.3 STATE MACHINES
        • 5.3.1 The Problem
        • 5.3.2 The Algorithm
        • 5.3.3 Pseudo-Code
        • 5.3.4 Data Structures and Interfaces
        • 5.3.5 On the CD
        • 5.3.6 Performance
        • 5.3.7 Implementation Notes
        • 5.3.8 Hard-Coded FSM
        • 5.3.9 Hierarchical State Machines
        • 5.3.10 Combining Decision Trees and State Machines
      • 5.4 FUZZY LOGIC
        • 5.4.1 Introduction to Fuzzy Logic
        • 5.4.2 Fuzzy Logic Decision Making
        • 5.4.3 Fuzzy State Machines
      • 5.5 MARKOV SYSTEMS
        • 5.5.1 Markov Processes
        • 5.5.2 Markov State Machine
      • 5.6 GOAL-ORIENTED BEHAVIOR
        • 5.6.1 Goal-Oriented Behavior
        • 5.6.2 Simple Selection
        • 5.6.3 Overall Utility
        • 5.6.4 Timing
        • 5.6.5 Overall Utility GOAP
        • 5.6.6 GOAP with IDA*
        • 5.6.7 Smelly GOB
      • 5.7 RULE-BASED SYSTEMS
        • 5.7.1 The Problem
        • 5.7.2 The Algorithm
        • 5.7.3 Pseudo-Code
        • 5.7.4 Data Structures and Interfaces
        • 5.7.5 Implementation Notes
        • 5.7.6 Rule Arbitration
        • 5.7.7 Unification
        • 5.7.8 Rete
        • 5.7.9 Extensions
        • 5.7.10 Where Next
      • 5.8 BLACKBOARD ARCHITECTURES
        • 5.8.1 The Problem
        • 5.8.2 The Algorithm
        • 5.8.3 Pseudo-Code
        • 5.8.4 Data Structures and Interfaces
        • 5.8.5 Performance
        • 5.8.6 Other Things Are Blackboard Systems
      • 5.9 SCRIPTING
        • 5.9.1 Language Facilities
        • 5.9.2 Embedding
        • 5.9.3 Choosing a Language
        • 5.9.4 A Language Selection
        • 5.9.5 Rolling Your Own
        • 5.9.6 Scripting Languages and Other AI
      • 5.10 ACTION EXECUTION
        • 5.10.1 Types of Action
        • 5.10.2 The Algorithm
        • 5.10.3 Pseudo-Code
        • 5.10.4 Data Structures and Interfaces
        • 5.10.5 Implementation Notes
        • 5.10.6 Performance
        • 5.10.7 Putting It All Together
    • 6 TACTICAL AND STRATEGIC AI
      • 6.1 WAYPOINT TACTICS
        • 6.1.1 Tactical Locations
        • 6.1.2 Using Tactical Locations
        • 6.1.3 Generating the Tactical Properties of a Waypoint
        • 6.1.4 Automatically Generating the Waypoints
        • 6.1.5 The Condensation Algorithm
      • 6.2 TACTICAL ANALYSES
        • 6.2.1 Representing the Game Level
        • 6.2.2 Simple Influence Maps
        • 6.2.3 Terrain Analysis
        • 6.2.4 Learning with Tactical Analyses
        • 6.2.5 A Structure for Tactical Analyses
        • 6.2.6 Map Flooding
        • 6.2.7 Convolution Filters
        • 6.2.8 Cellular Automata
      • 6.3 TACTICAL PATHFINDING
        • 6.3.1 The Cost Function
        • 6.3.2 Tactic Weights and Concern Blending
        • 6.3.3 Modifying the Pathfinding Heuristic
        • 6.3.4 Tactical Graphs for Pathfinding
        • 6.3.5 Using Tactical Waypoints
      • 6.4 COORDINATED ACTION
        • 6.4.1 Multi-Tier AI
        • 6.4.2 Emergent Cooperation
        • 6.4.3 Scripting Group Actions
        • 6.4.4 Military Tactics
    • 7 LEARNING
      • 7.1 LEARNING BASICS
        • 7.1.1 Online or Offline Learning
        • 7.1.2 Intra-Behavior Learning
        • 7.1.3 Inter-Behavior Learning
        • 7.1.4 A Warning
        • 7.1.5 Over-learning
        • 7.1.6 The Zoo of Learning Algorithms
        • 7.1.7 The Balance of Effort
      • 7.2 PARAMETER MODIFICATION
        • 7.2.1 The Parameter Landscape
        • 7.2.2 Hill Climbing
        • 7.2.3 Extensions to Basic Hill Climbing
        • 7.2.4 Annealing
      • 7.3 ACTION PREDICTION
        • 7.3.1 Left or Right
        • 7.3.2 Raw Probability
        • 7.3.3 String Matching
        • 7.3.4 N-Grams
        • 7.3.5 Window Size
        • 7.3.6 Hierarchical N-Grams
        • 7.3.7 Application in Combat
      • 7.4 DECISION LEARNING
        • 7.4.1 Structure of Decision Learning
        • 7.4.2 What Should You Learn?
        • 7.4.3 Three Techniques
      • 7.5 DECISION TREE LEARNING
        • 7.5.1 ID3
        • 7.5.2 ID3 with Continuous Attributes
        • 7.5.3 Incremental Decision Tree Learning
      • 7.6 REINFORCEMENT LEARNING
        • 7.6.1 The Problem
        • 7.6.2 The Algorithm
        • 7.6.3 Pseudo-Code
        • 7.6.4 Data Structures and Interfaces
        • 7.6.5 Implementation Notes
        • 7.6.6 Performance
        • 7.6.7 Tailoring Parameters
        • 7.6.8 Weaknesses and Realistic Applications
        • 7.6.9 Other Ideas in Reinforcement Learning
      • 7.7 ARTIFICIAL NEURAL NETWORKS
        • 7.7.1 Overview
        • 7.7.2 The Problem
        • 7.7.3 The Algorithm
        • 7.7.4 Pseudo-Code
        • 7.7.5 Data Structures and Interfaces
        • 7.7.6 Implementation Caveats
        • 7.7.7 Performance
        • 7.7.8 Other Approaches
    • 8 BOARD GAMES
      • 8.1 GAME THEORY
        • 8.1.1 Types of Games
        • 8.1.2 The Game Tree
      • 8.2 MINIMAXING
        • 8.2.1 The Static Evaluation Function
        • 8.2.2 Minimaxing
        • 8.2.3 The Minimaxing Algorithm
        • 8.2.4 Negamaxing
        • 8.2.5 AB Pruning
        • 8.2.6 The AB Search Window
        • 8.2.7 Negascout
      • 8.3 TRANSPOSITION TABLES AND MEMORY
        • 8.3.1 Hashing Game States
        • 8.3.2 What to Store in the Table
        • 8.3.3 Hash Table Implementation
        • 8.3.4 Replacement Strategies
        • 8.3.5 A Complete Transposition Table
        • 8.3.6 Transposition Table Issues
        • 8.3.7 Using Opponent's Thinking Time
      • 8.4 MEMORY-ENHANCED TEST ALGORITHMS
        • 8.4.1 Implementing Test
        • 8.4.2 The MTD Algorithm
        • 8.4.3 Pseudo-Code
      • 8.5 OPENING BOOKS AND OTHER SET PLAYS
        • 8.5.1 Implementing an Opening Book
        • 8.5.2 Learning for Opening Books
        • 8.5.3 Set Play Books
      • 8.6 FURTHER OPTIMIZATIONS
        • 8.6.1 Iterative Deepening
        • 8.6.2 Variable Depth Approaches
      • 8.7 TURN-BASED STRATEGY GAMES
        • 8.7.1 Impossible Tree Size
        • 8.7.2 Real-Time AI in a Turn-Based Game
  • PART III SUPPORTING TECHNOLOGIES
    • 9 EXECUTION MANAGEMENT
      • 9.1 SCHEDULING
        • 9.1.1 The Scheduler
        • 9.1.2 Interruptible Processes
        • 9.1.3 Load-Balancing Scheduler
        • 9.1.4 Hierarchical Scheduling
        • 9.1.5 Priority Scheduling
      • 9.2 ANYTIME ALGORITHMS
      • 9.3 LEVEL OF DETAIL
        • 9.3.1 Graphics Level of Detail
        • 9.3.2 AI LOD
        • 9.3.3 Scheduling LOD
        • 9.3.4 Behavioral LOD
        • 9.3.5 Group LOD
        • 9.3.6 In Summary
    • 10 WORLD INTERFACING
      • 10.1 COMMUNICATION
      • 10.2 GETTING KNOWLEDGE EFFICIENTLY
        • 10.2.1 Polling
        • 10.2.2 Events
        • 10.2.3 Determining What Approach to Use
      • 10.3 EVENT MANAGERS
        • 10.3.1 Implementation
        • 10.3.2 Event Casting
        • 10.3.3 Inter-Agent Communication
      • 10.4 POLLING STATIONS
        • 10.4.1 Pseudo-Code
        • 10.4.2 Performance
        • 10.4.3 Implementation Notes
        • 10.4.4 Abstract Polling
      • 10.5 SENSE MANAGEMENT
        • 10.5.1 Faking It
        • 10.5.2 What Do I Know?
        • 10.5.3 Sensory Modalities
        • 10.5.4 Region Sense Manager
        • 10.5.5 Finite Element Model Sense Manager
    • 11 TOOLS AND CONTENT CREATION
        • 11.0.1 Toolchains Limit AI
        • 11.0.2 Where AI Knowledge Comes from
      • 11.1 KNOWLEDGE FOR PATHFINDING AND WAYPOINT TACTICS
        • 11.1.1 Manually Creating Region Data
        • 11.1.2 Automatic Graph Creation
        • 11.1.3 Geometric Analysis
        • 11.1.4 Data Mining
      • 11.2 KNOWLEDGE FOR MOVEMENT
        • 11.2.1 Obstacles
        • 11.2.2 High-Level Staging
      • 11.3 KNOWLEDGE FOR DECISION MAKING
        • 11.3.1 Object Types
        • 11.3.2 Concrete Actions
      • 11.4 THE TOOLCHAIN
        • 11.4.1 Data-Driven Editors
        • 11.4.2 AI Design Tools
        • 11.4.3 Remote Debugging
        • 11.4.4 Plug-Ins
  • PART IV DESIGNING GAME AI
    • 12 DESIGNING GAME AI
      • 12.1 THE DESIGN
        • 12.1.1 Example
        • 12.1.2 Evaluating the Behaviors
        • 12.1.3 Selecting Techniques
        • 12.1.4 The Scope of One Game
      • 12.2 SHOOTERS
        • 12.2.1 Movement and Firing
        • 12.2.2 Decision Making
        • 12.2.3 Perception
        • 12.2.4 Pathfinding and Tactical AI
        • 12.2.5 Shooter-Like Games
      • 12.3 DRIVING
        • 12.3.1 Movement
        • 12.3.2 Pathfinding and Tactical AI
        • 12.3.3 Driving-Like Games
      • 12.4 REAL-TIME STRATEGY
        • 12.4.1 Pathfinding
        • 12.4.2 Group Movement
        • 12.4.3 Tactical and Strategic AI
        • 12.4.4 Decision Making
      • 12.5 SPORTS
        • 12.5.1 Physics Prediction
        • 12.5.2 Playbooks and Content Creation
      • 12.6 TURN-BASED STRATEGY GAMES
        • 12.6.1 Timing
        • 12.6.2 Helping the Player
    • 13 AI-BASED GAME GENRES
      • 13.1 TEACHING CHARACTERS
        • 13.1.1 Representing Actions
        • 13.1.2 Representing the World
        • 13.1.3 Learning Mechanism
        • 13.1.4 Predictable Mental Models and Pathological States
      • 13.2 FLOCKING AND HERDING GAMES
        • 13.2.1 Making the Creatures
        • 13.2.2 Tuning Steering for Interactivity
        • 13.2.3 Steering Behavior Stability
        • 13.2.4 Ecosystem Design
  • APPENDIX
    • A REFERENCES
    • A.1 BOOKS, PERIODICALS, AND PAPERS
    • A.2 GAMES
  • INDEX

About the Author

Ian Millington is a partner of IPR Ventures, a consulting company developing next-generation AI technologies for entertainment, modeling, and simulation. Previously he founded Mindlathe Ltd, the largest specialist AI middleware company in computer games, working with on a huge range of game genres and technologies. He has a long background in AI, including PhD research in complexity theory and natural computing. He has published academic and professional papers and articles on topics ranging from paleontology to hypertext.

Reviews

"Ian Millington crosses the boundary between academic and professional game AI with his book Artificial Intelligence for Games. Most books either lack academic rigor or are rigorous with algorithms that won't work under the CPU constraints imposed by modern games. This book walks a line between the two and does it well. It explains algorithms rigorously while also discussing appropriate implementation details such as scheduling AI over time and using the right data structures. I will be using this book for my Game AI course." -Jessica D. Bayliss, Ph.D., Rochester Institute of Technology

"This is the first serious attempt to create a comprehensive reference for all game AI practices, terminology, and know-how. Works like this are badly needed by the maturing video games industry. Systematic yet accessible, it is a must-have for any student or professional." -Marcin Chady, Ph.D., Radical Entertainment

"This book promises to be the closest I've seen to what is needed in the field. I would highly recommend it for people in the industry." -John Laird, University of Michigan

"Ian Millington's book is a comprehensive reference to the most widely used techniques in game AI today. Any game developer working on AI will learn something from this book, and game producers should make sure their AI programmers have a copy." -Dr. Ian Lane Davis, Mad Doc Software

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