Artificial Intelligence for Games (Hardcover)
暫譯: 遊戲中的人工智慧 (精裝版)

Description

Creating robust artificial intelligence is one of the greatest challenges for game developers. The commercial success of a game is often dependent upon the quality of the AI, yet the engineering of AI is often begun late in the development process and is frequently misunderstood. In this book, Ian Millington brings extensive professional experience to the problem of improving the quality of AI in games. A game developer since 1987, he was founder of Mindlathe Ltd., at the time the largest specialist AI company in gaming. Ian shows how to think about AI as an integral part of game play. He describes numerous examples from real games and explores the underlying ideas through detailed case studies. He goes further to introduce many techniques little used by developers today. The book's CD-ROM contains a library of C++ source code and demonstration programs, and provides access to a website with a complete commercial source code library of AI algorithms and techniques.

 

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