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Emotion Notions: Modeling Personality in Game Character AI
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Emotion Notions: Modeling Personality in Game Character AI (Books & other media combined)

£41.99
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Synopsis

Learn how to create more realistic and believable characters for video

games using AI programming techniques.Written for novice to experienced

game developers, "Emotion Notions: Modeling Personality in Game

Character AI" shows you how to model human personality, mood, and

emotion into your video game characters. Adding personality to game

characters allows them to portray intention, a necessary component of

believability. You'll examine the theory and concepts behind video

game artificial intelligence, such as believability, interaction, engagement,

and immersion. Using 2D sprite-based games created with Microsoft

XNA, you'll explore these game AI concepts in a simple game environment

and learn how to apply them to all games. You'll learn how to

model individuality, emotion, mood, and more to make your game

characters more convincing and human-like as you work step-by-step

through hands-on projects. All the source code and assets needed to

complete the projects is included on the accompanying CD-ROM.

Dr. Erik Vick holds an M.S.C.S (UCF, 1998) and a Ph.D. in Modeling and Simulation specialized in Artificial Intelligence (UCF, 2005) and has extensive experience in artificial characters and game design, including a stint at Electronic Arts as a technical game designer, artificial intelligence researcher, and software engineer. Dr. Vick is an Assistant Professor in the Information Technology Department's Game Design and Development program at the Rochester Institute of Technology. He also served at the Chief Technical Officer for an international software company focused on mobile devices. He taught Computer Science at UCF from 1998 to 2000, and Digital Media Faculty from 2001 to 2004 and 2005 to 2007. Dr. Vick is the author of Emotion Notions: Modeling Personality in Game AI (Course Technology PTR).

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Location 1st Book Each additional book Average Delivery Time
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