Behavioral Mathematics for Game AI / Edition 1

Behavioral Mathematics for Game AI / Edition 1

by Dave Mark
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Cengage Learning

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Behavioral Mathematics for Game AI / Edition 1

Human behavior is never an exact science, making the design and programming of artificial intelligence that seeks to replicate human behavior difficult. Usually, the answers cannot be found in sterile algorithms that are often the focus of artificial intelligence programming. However, by analyzing why people behave the way we do, we can break down the process into increasingly smaller components. We can model many of those individual components in the language of logic and mathematics and then reassemble them into larger, more involved decision-making processes. Drawing from classical game theory, "Behavioral Mathematics for Game AI" covers both the psychological foundations of human decisions and the mathematical modeling techniques that AI designers and programmers can use to replicate them. With examples from both real life and game situations, you'll explore topics such as utility, the fallacy of rational behavior, and the inconsistencies and contradictions that human behavior often exhibits. You'll examine various ways of using statistics, formulas, and algorithms to create believable simulations and to model these dynamic, realistic, and interesting behaviors in video games. Finally, you'll be introduced to a number of tools you can use in conjunction with standard AI algorithms to make it easier to utilize the mathematical models.

Product Details

ISBN-13: 9781584506843
Publisher: Cengage Learning
Publication date: 03/05/2009
Series: Applied Mathematics Series
Pages: 459
Sales rank: 833,585
Product dimensions: 7.40(w) x 9.30(h) x 4.10(d)

Table of Contents

Part I: Introduction 1: Why Behavioral Mathematics 2: Observing the World 3: Converting Behaviors to Algorithms Part II: Decision Theory 4: Defining Decision Theory 5: Game Theory 6: Rational vs. Irrational Behavior 7: The Concept of Utility 8: Marginal Utility 9: Relative Utility Part III: Mathematical Modeling 10: Mathematical Functions 11: Probability Distributions 12: Response Curves 13: Factor Weighting Part IV: Behavioral Algorithms 14: Modeling Individual Decisions 15: Changing a Decision 16: Variation in Choice

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