AI Game Programming Wisdom 2 / Edition 1

AI Game Programming Wisdom 2 / Edition 1

by Steve Rabin
     
 

AI Game Programming Wisdom 2, the second volume in this cutting-edge series, is packed with all new tricks, techniques, algorithms, architectures, and philosophies all written by industry experts! The wealth of knowledge and expertise in this volume is sure to surpass your expectations. As with the first volume, this book is designed to provide practical advice for

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Overview

AI Game Programming Wisdom 2, the second volume in this cutting-edge series, is packed with all new tricks, techniques, algorithms, architectures, and philosophies all written by industry experts! The wealth of knowledge and expertise in this volume is sure to surpass your expectations. As with the first volume, this book is designed to provide practical advice for building state-of-the-art game AI for commercial games; however, it also strives to help you look forward to leading-edge techniques that will be critical in future explorations. AI Game Programming Wisdom 2 provides advances, discoveries, and triumphs that will influence and drive game AI for the next decade. The breadth of experience and diverse backgrounds of the authors make this a truly global, cross-sectional resource for game AI. The book is divided into twelve comprehensive sections, including an all new speech recognition and dialogue section. There is also coverage of a wider variety of game genres and a cumulative index is included for easy cross referencing between volumes. This new volume alone is an indispensable tool, but together with volume 1, these books form a remarkable collection that no game AI programmer should be without!

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Product Details

ISBN-13:
9781584502890
Publisher:
Cengage Learning
Publication date:
12/09/2003
Series:
Charles River Media Game Development Series
Edition description:
BK&CD-ROM
Pages:
732
Product dimensions:
7.50(w) x 9.50(h) x 1.90(d)

Meet 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.

Table of Contents

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

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