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Artificial Intelligence for Games

Artificial Intelligence for Games by Ian Millington
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  • 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
CRC Press; April 2014
895 pages; ISBN 9781482267341
Read online, or download in secure PDF format
Title: Artificial Intelligence for Games
Author: Ian Millington
 
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