Product Details

• Paperback: 390 pages
• Publisher: O'Reilly Media, Inc. (July 23, 2004)
• Language: English
• ISBN-10: 0596005555
• ISBN-13: 978-0596005559

Product Description
Advances in 3D visualization and physics-based simulation technology make it possible for game developers to create compelling, visually immersive gaming environments that were only dreamed of years ago. But today's game players have grown in sophistication along with the games they play. It's no longer enough to wow your players with dazzling graphics; the next step in creating even more immersive games is improved artificial intelligence, or AI. Fortunately, advanced AI game techniques are within the grasp of every game developer--not just those who dedicate their careers to AI. If you're new to game programming or if you're an experienced game programmer who needs to get up to speed quickly on AI techniques, you'll find AI for Game Developers to be the perfect starting point for understanding and applying AI techniques to your games. Written for the novice AI programmer, AI for Game Developers introduces you to techniques such as finite state machines, fuzzy logic, neural networks, and many others, in straightforward, easy-to-understand language, supported with code samples throughout the entire book (written in C/C++). From basic techniques such as chasing and evading, pattern movement, and flocking to genetic algorithms, the book presents a mix of deterministic (traditional) and non-deterministic (newer) AI techniques aimed squarely at beginners AI developers. Other topics covered in the book include:

• Potential function based movements: a technique that handles chasing, evading swarming, and collision avoidance simultaneously
• Basic pathfinding and waypoints, including an entire chapter devoted to the A* pathfinding algorithm
• AI scripting
• Rule-based AI: learn about variants other than fuzzy logic and finite state machines
• Basic probability
• Bayesian techniques

Unlike other books on the subject, AI for Game Developers doesn't attempt to cover every aspect of game AI, but to provide you with usable, advanced techniques you can apply to your games right now. If you've wanted to use AI to extend the play-life of your games, make them more challenging, and most importantly, make them more fun, then this book is for you.

About the Author
As a naval architect and marine engineer, David M. Bourg performs computer simulations and develops analysis tools that measure such things as hovercraft performance and the effect of waves on the motion of ships and boats. He teaches at the college level in the areas of ship design, construction and analysis. On occasion, David also lectures at high schools on topics such as naval architecture and software development. In addition to David's practical engineering background, he's professionally involved in computer game development and consulting through his company, Crescent Vision Interactive . Current projects include a massively multiplayer online role-playing game, several Java-based multiplayer games, and the porting of Hasbro's "Breakout" to the Macintosh.

Glenn Seemann is a veteran game programmer with over a dozen games to his credit, for Mac and Windows systems. He's a co-founder with David Bourg, of Crescent Vision Interactive, a game development company specializing in cross-platform games

Product Details:

Author : Mark Watson
Pages : 124
Publication Date : November 18, 2005

Book Excerpts:

This book provides the theory of many useful techniques for AI programming. Readers should find this a fun book to work through. In the style of a "cookbook", the chapters in this book can be studied in any order. Each chapter follows the same pattern: a motivation for learning a technique, some theory for the technique, and a Java example program that readers can experiment with.

Subjects discussed in this book include search algorithms, natural language processing, expert systems, genetic algorithms, neural networks, machine learning, statistical natural language processing and spam email detection using Bayesian rules.

There are relatively few source code listings in this book, but complete example programs that are discussed in the text should have been included in the same ZIP file that contained this web book. Should reader find this this web book without the examples, she can download an up to date version of the book and examples on the Open Content page of

In order to discuss some of the example code in this book, the author used Unified Modeling Language (UML) class diagrams. These diagrams were created using the TogetherJ modeling tool.

Intended Audience:

This book was written for both professional programmers and home hobbyists who already know how to program in Java and who want to learn practical AI programming techniques.

Product Details

• Hardcover: 1132 pages
• Publisher: Prentice Hall; 2 edition (December 30, 2002)
• Language: English
• ISBN-10: 0137903952
• ISBN-13: 978-0137903955

The long-anticipated revision of this best-selling book offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First-order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural Networks. Reinforcement Learning. Knowledge in Learning. Agents that Communicate. Practical Communication in English. Perception. Robotics. For those interested in artificial intelligence.

Book Info
Presents a unified, coherent picture of the AI field based on the ideal of intelligent agents, and shows how to build them using the AI methods. DLC: Artificial intelligence. --This text refers to an out of print or unavailable edition of this title.

The publisher, Prentice-Hall Engineering/Science/Mathematics
The most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. --This text refers to an out of print or unavailable edition of this title.

From the Back Cover

The first edition of Artificial Intelligence: A Modern Approach has become a classic in the AI literature. It has been adopted by over 600 universities in 60 countries, and has been praised as the definitive synthesis of the field.

In the second edition, every chapter has been extensively rewritten. Significant new material has been introduced to cover areas such as constraint satisfaction, fast propositional inference, planning graphs, internet agents, exact probabilistic inference, Markov Chain Monte Carlo techniques, Kalman filters, ensemble learning methods, statistical learning, probabilistic natural language models, probabilistic robotics, and ethical aspects of AI.

The book is supported by a suite of online resources including source code, figures, lecture slides, a directory of over 800 links to "AI on the Web," and an online discussion group. All of this is available at:
aima.cs.berkeley.edu

About the Author

Stuart Russell was born in 1962 in Portsmouth, England. He received his B.A. with first-class honours in physics from Oxford University in 1982, and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is a professor of computer science, director of the Center for Intelligent Systems, and holder of the Smith-Zadeh Chair in Engineering. In 1990, he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was cowinner of the Computers and Thought Award. He was a 1996 Miller Professor of the University of California and was appointed to a Chancellor's Professorship in 2000. In 1998, he gave the Forsythe Memorial Lectures at Stanford University. He is a Fellow and former Executive Council member of the American Association for Artificial Intelligence. He has published over 100 papers on a wide range of topics in artificial intelligence. His other books include The Use of Knowledge in Analogy and Induction and (with Eric Wefald) Do the Right Thing: Studies in Limited Rationality.

Peter Norvig is director of Search Quality at Google, Inc. He is a Fellow and Executive Council member of the American Association for Artificial Intelligence. Previously, he was head of the Computational Sciences Division at NASA Ames Research Center, where he oversaw NASA's research and development in artificial intelligence and robotics. Before that he served as chief scientist at Junglee, where he helped develop one of the first Internet information extraction services, and as a senior scientist at Sun Microsystems Laboratories working on intelligent information retrieval. He received a B.S. in applied mathematics from Brown University and a Ph.D. in computer science from the University of California at Berkeley. He has been a professor at the University of Southern California and a research faculty member at Berkeley. He has over 50 publications in computer science including the books Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX.