Unity 5.x Game AI Programming Cookbook

Packt Publishing Limited
  • 1. Auflage
  • |
  • erschienen am 31. März 2016
  • |
  • 278 Seiten
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
978-1-78355-358-7 (ISBN)
Build and customize a wide range of powerful Unity AI systems with over 70 hands-on recipes and techniquesAbout This BookEmpower your agent with decision making capabilities using advanced minimaxing and Negamaxing techniquesDiscover how AI can be applied to a wide range of games to make them more interactive.Instigate vision and hearing abilities in your agent through collider based and graph based systemsWho This Book Is ForThis book is intended for those who already have a basic knowledge of Unity and are eager to get more tools under their belt to solve AI and gameplay-related problems.What You Will LearnUse techniques such as A*and A*mbush to empower your agents with path finding capabilities.Create a representation of the world and make agents navigate itConstruct decision-making systems to make the agents take different actionsMake different agents coordinate actions and create the illusion of technical behaviorSimulate senses and apply them in an awareness systemDesign and implement AI in board games such as Tic-Tac-Toe and CheckersImplement efficient prediction mechanism in your agents with algorithms such as N-Gram predictor and naive Bayes classifierUnderstand and analyze how the influence maps work.In DetailUnity 5 comes fully packaged with a toolbox of powerful features to help game and app developers create and implement powerful game AI. Leveraging these tools via Unity's API or built-in features allows limitless possibilities when it comes to creating your game's worlds and characters. This practical Cookbook covers both essential and niche techniques to help you be able to do that and more.This Cookbook is engineered as your one-stop reference to take your game AI programming to the next level. Get to grips with the essential building blocks of working with an agent, programming movement and navigation in a game environment, and improving your agent's decision making and coordination mechanisms - all through hands-on examples using easily customizable techniques. Discover how to emulate vision and hearing capabilities for your agent, for natural and humanlike AI behaviour, and improve them with the help of graphs. Empower your AI with decision-making functions through programming simple board games such as Tic-Tac-Toe and Checkers, and orchestrate agent coordination to get your AIs working together as one.Style and approachThis recipe-based guide will take you through implementing various AI algorithms. Each topic is explained and placed among other related techniques, sometimes building on the knowledge from previous chapters. There are also references to more technical books and papers, so you can dig deeper if you want to.
  • Englisch
  • Birmingham
  • |
  • Großbritannien
978-1-78355-358-7 (9781783553587)
1783553588 (1783553588)
weitere Ausgaben werden ermittelt
Jorge Palacios is a software developer with seven years of professional experience. He has committed the last four years to game development working in various positions; from tool developer, to lead programmer. His main focus is AI and gameplay programming, and currently he works with Unity and HTML5. He's also a game development instructor, speaker, and game jam organizer.
You can find more about him on http://jorge.palacios.co
  • Cover
  • Copyright
  • Credits
  • About the Author
  • About the Reviewers
  • www.PacktPub.com
  • Table of Contents
  • Preface
  • Chapter 1: Behaviors - Intelligent Movement
  • Introduction
  • Creating the behavior template
  • Pursuing and evading
  • Arriving and leaving
  • Facing objects
  • Wandering around
  • Following a path
  • Avoiding agents
  • Avoiding walls
  • Blending behaviors by weight
  • Blending behaviors by priority
  • Combining behaviors using a steering pipeline
  • Shooting a projectile
  • Predicting a projectile's landing spot
  • Targeting a projectile
  • Creating a jump system
  • Chapter 2: Navigation
  • Introduction
  • Representing the world with grids
  • Representing the world with Dirichlet domains
  • Representing the world with points of visibility
  • Representing the world with a self-made navigation mesh
  • Finding your way out of a maze with DFS
  • Finding the shortest path in a grid with BFS
  • Finding the shortest path with Dijkstra
  • Finding the best-promising path with A*
  • Improving A* for memory: IDA*
  • Planning navigation in several frames: time-sliced search
  • Smoothing a path
  • Chapter 3: Decision Making
  • Introduction
  • Choosing through a decision tree
  • Working a finite-state machine
  • Improving FSMs: hierarchical finite-state machines
  • Combining FSMs and decision trees
  • Implementing behavior trees
  • Working with fuzzy logic
  • Representing states with numerical values: Markov system
  • Making decisions with goal-oriented behaviors
  • Chapter 4: Coordination and Tactics
  • Introduction
  • Handling formations
  • Extending A* for coordination: A*mbush
  • Creating good waypoints
  • Analyzing waypoints by height
  • Analyzing waypoints by cover and visibility
  • Exemplifying waypoints for decision making
  • Influence maps
  • Improving influence with map flooding
  • Improving influence with convolution filters
  • Building a fighting circle
  • Chapter 5: Agent Awareness
  • Introduction
  • The seeing function using a collider-based system
  • The hearing function using a collider-based system
  • The smelling function using a collider-based system
  • The seeing function using a graph-based system
  • The hearing function using a graph-based system
  • The smelling function using a graph-based system
  • Creating awareness in a stealth game
  • Chapter 6.: Board Games AI
  • Introduction
  • Working with the game-tree class
  • Introducing Minimax
  • Negamaxing
  • AB Negamaxing
  • Negascouting
  • Implementing a tic-tac-toe rival
  • Implementing a checkers rival
  • Chapter 7: Learning Techniques
  • .Introduction
  • Predicting actions with an N-Gram predictor
  • Improving the predictor: Hierarchical N-Gram
  • Learning to use Naïve Bayes classifiers
  • Learning to use decision trees
  • Learning to use reinforcement
  • Learning to use artificial neural networks
  • Creating emergent particles using a harmony search
  • Chapter 8: Miscellaneous
  • Introduction
  • Handling random numbers better
  • Building an air-hockey rival
  • Devising a table-football competitor
  • Creating mazes procedurally
  • Implementing a self-driving car
  • Managing race difficulty using a rubber-banding system
  • Index
  • _GoBack

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