AI Game Programming Wisdom 2

Steve Rabin is a Principal Software Engineer at Nintendo of America, where he researches new techniques for Nintendo's next generation systems, develops tools, and supports Nintendo developers. Before Nintendo, Steve worked primarily as an AI engineer at several Seattle start-ups including Gas Powered Games,WizBang Software Productions, and Surreal Software. He managed and edited the AI Game Programming Wisdom series of books, as well as the book Introduction to Game Development, and has over a dozen articles published in the Game Programming Gems series. He's spoken at the Game Developers Conference and moderates the AI roundtables. Steve teaches artificial intelligence at both the University of Washington Extension and at the DigiPen Institute of Technology. He earned a B.S. in computer engineering and an M.S. in computer science, both from the University of Washington.

Preface
Acknowledgments
About the Cover Images
Author Bios
Section 1 General Wisdom
1.1 The Evolution of Game AI
1.2 The Illusion of Intelligence
1.3 Solving the Right Problem
1.4 12 Tips from the Trenches
Section 2 Useful Techniques and Specialized Systems
2.1 Building an AI Diagnostic Toolset
2.2 A General-Purpose Trigger System
2.3 A Data-Driven Architecture for Animation Selection
2.4 Realistic Character Behavior with Prioritized, Categorized Animation
2.5 Designing a GUI Tool to Aid in the Development of Finite-State Machines
2.6 The Beauty of Response Curves
2.7 Simple and Efficient Line-of-Sight for 3D Landscapes
2.8 An Open-Source Fuzzy Logic Library
Section 3 Pathfinding with A*
3.1 Basic A* Pathfinding Made Simple
3.2 Generic A* Pathfinding
3.3 Pathfinding Design Architecture
3.4 How to Achieve Lightning-Fast A*
3.5 Practical Optimizations for A* Path Generation
Section 4 Pathfinding and Movement
4.1 Simple, Cheap Pathfinding
4.2 Preprocessed Solution for Open Terrain Navigation
4.3 Building a Near-Optimal Navigation Mesh
4.4 Realistic Turning between Waypoints
4.5 Navigating Doors, Elevators, Ledges, and Other Obstacles
4.6 Simple Swarms as an Alternative to Flocking
Section 5 Tactical Issues and Intelligent Group Movement
5.1 Strategic and Tactical Reasoning with Waypoints
5.2 Recognizing Strategic Dispositions: Engaging the Enemy
5.3 Squad Tactics: Team AI and Emergent Maneuvers
5.4 Squad Tactics: Planned Maneuvers
5.5 Tactical Team AI Using a Command Hierarchy
5.6 Formations
Section 6 General Purpose Architectures
6.1 Architecting a Game AI
6.2 An Efficient AI Architecture Using Prioritized Task Categories
6.3 An Architecture Based on Load Balancing
6.4 A Simple Inference Engine for a Rule-Based Architecture
6.5 Implementing a State Machine Language
6.6 Enhancing a State Machine Language through Messaging
Section 7 Decision-Making Architectures
7.1 Blackboard Architectures
7.2 Introduction to Bayesian Networks and Reasoning Under Uncertainty
7.3 A Rule-Based Architecture Using the Dempster-Shafer Theory
7.4 An Optimized Fuzzy Logic Architecture for Decision-Making
7.5 A Flexible Goal-Based Planning Architecture
Section 8 FPS, RTS, and RPG AI
8.1 First-Person Shooter Al Architecture
8.2 Architecting an RTS AI
8.3 An Economic Approach to Goal-Directed Reasoning in an RTS
8.4 The Basics of Ranged Weapon Combat
8.5 Level-Of-Detail AI for a Large Role-Playing Game
8.6 A Dynamic Reputation System Based on Event Knowledge
Section 9 Racing and Sports AI
9.1 Representing a Racetrack for the AI
9.2 Racing AI Logic
9.3 Training an AI to Race
9.4 Competitive AI Racing under Open Street Conditions
9.5 Camera AI for Replays
9.6 Simulating Real Animal Behavior
9.7 Agent Cooperation in FSMs for Baseball
9.8 Intercepting a Ball
Section 10 Scripting
10.1 Scripting: Overview and Code Generation
10.2 Scripting: The Interpreter Engine
10.3 Scripting: System Integration
10.4 Creating Scripting Languages for Nonprogrammers
10.5 Scripting for Undefined Circumstances
10.6 The Perils of Al Scripting
10.7 How Not to Implement a Basic Scripting Language
Section 11 Learning
11.1 Learning and Adaptation
11.2 Varieties of Learning
11.3 GoCap: Game Observation Capture
11.4 Pattern Recognition with Sequential Prediction
11.5 Using N-Gram Statistical Models to Predict Player Behavior
11.6 Practical Natural Language Learning
11.7 Testing Undefined Behavior as a Result of Learning
11.8 Imitating Random Variations in Behavior Using a Neural Network
11.9 Genetic Algorithms: Evolving the Perfect Troll
11.10 The Dark Art of Neural Networks
About the CD-ROM
Index