AI Game Programming Wisdom 2 Hardcover – Dec 9 2003
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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
About the Author
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.
Top Customer Reviews
AI Game Programming Wisdom 2 is structured very much the same as the first book in the series. The articles are broken into similar sections, with the addition of a new section on finite-state-machines and splitting the section on learning into two. Having already covered the A* algorithm, its various optimizations, and navigation meshes in the previous books those topics are virtually absent here (though they may be buried as a secondary topic in some of the path-finding articles). I thought that the series had exhausted discussion of finite-state machines, but the editors managed to include a couple of interesting articles. The demand for more realistic AI behavior in character-based games in growing. And while a full simulation of emotions and human characteristics is unteneable, at the moment, there are some interesting ideas in the Learning section of the book e.g.Read more ›
Ok, let us not try to get bogged down into semantics. The book does have many nice, state of the art (2004) methods for handling diverse problems arising in game coding. For example, the section on speech recognition is quite well done. The main thing to take away from that section is that you need to restrict the problem so that the range of possible user responses is limited. Which may not necessarily be what that author intended. But from your point of view of easier and more robust coding, this is the pragmatic way to use speech recognition.
Of course, an AI purist might argue that what I have just suggested is not really AI. I agree. I use the speech recognition as just one example. You may derive the greatest benefit from this book if you deprecate the AI aspect and just see what methods you can usefully cull. Always remember that true AI is really hard. You are writing games. NOT research. Leave that to others.
Most Helpful Customer Reviews on Amazon.com (beta)
AI Game Programming Wisdom 2 is structured very much the same as the first book in the series. The articles are broken into similar sections, with the addition of a new section on finite-state-machines and splitting the section on learning into two. Having already covered the A* algorithm, its various optimizations, and navigation meshes in the previous books those topics are virtually absent here (though they may be buried as a secondary topic in some of the path-finding articles). I thought that the series had exhausted discussion of finite-state machines, but the editors managed to include a couple of interesting articles. The demand for more realistic AI behavior in character-based games in growing. And while a full simulation of emotions and human characteristics is unteneable, at the moment, there are some interesting ideas in the Learning section of the book e.g. "Motivational Graphs: A New Architecture for Complex Behavior Simulation.".
I can't say that this book is indispensible from an implementation perspective. Most of the articles are simply too vague - though there are notable exceptions throughout the entire series of books. What I found most useful in these articles was that they stimulated thought and helped to generate discussion among the programmers and designers at work.
What else is wrong with this reviewer's reading/writing skills that contributed to their negative review of this book? If you can't write clearly...
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