Explore various recipes to build games using popular artificial intelligence techniques and algorithms such as Navmesh navigation A*, DFS, and UCB1 Key Features Explore different algorithms for creating decision-making agents that go beyond simple behaviors and movement Discover the latest features of the NavMesh API for scripting intelligent behaviour in your game characters Create games that are non-predictable and dynamic and have a high replayability factor Book Description Interactive and engaging games come with intelligent enemies, and this intellectual behavior is combined with a variety of techniques collectively referred to as Artificial Intelligence. Exploring Unity's API, or its built-in features, allows limitless possibilities when it comes to creating your game's worlds and characters. This cookbook covers both essential and niche techniques to help you take your AI programming to the next level. To start with, you’ll quickly run through the essential building blocks of working with an agent, programming movement, and navigation in a game environment, followed by improving your agent's decision-making and coordination mechanisms – all through hands-on examples using easily customizable techniques. You’ll then discover how to emulate the vision and hearing capabilities of your agent for natural and humanlike AI behavior, and later improve the agents with the help of graphs. This book also covers the new navigational mesh with improved AI and pathfinding tools introduced in the Unity 2018 update. You’ll empower your AI with decision-making functions by programming simple board games, such as tic-tac-toe and checkers, and orchestrate agent coordination to get your AIs working together as one. By the end of this book, you’ll have gained expertise in AI programming and developed creative and interactive games. What you will learn Create intelligent pathfinding agents with popular AI techniques such as A* and A*mbush Implement different algorithms for adding coordination between agents and tactical algorithms for different purposes Simulate senses so agents can make better decisions, taking account of the environment Explore different algorithms for creating decision-making agents that go beyond simple behaviors and movement Create coordination between agents and orchestrate tactics when dealing with a graph or terrain Implement waypoints by making a manual selector Who this book is for The Unity 2018 Artificial Intelligence Cookbook is for you if you are eager to get more tools under your belt to solve AI- and gameplay-related problems. Basic knowledge of Unity and prior knowledge of C# is an advantage.

Build and customize a wide range of powerful Unity AI systems with over 70 hands-on recipes and techniques About This Book Empower your agent with decision making capabilities using advanced minimaxing and Negamaxing techniques Discover 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 systems Who This Book Is For This 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 Learn Use 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 it Construct decision-making systems to make the agents take different actions Make different agents coordinate actions and create the illusion of technical behavior Simulate senses and apply them in an awareness system Design and implement AI in board games such as Tic-Tac-Toe and Checkers Implement efficient prediction mechanism in your agents with algorithms such as N-Gram predictor and naive Bayes classifier Understand and analyze how the influence maps work. In Detail Unity 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 approach This 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.

Unity 2018 provides game and app developers with a variety of tools to implement Artificial Intelligence(AI). Leveraging these tools via Unity's API allows limitless possibilities for creating your game's worlds and characters. This edition will break down AI into simple concepts to give you a fundamental understanding of the topic to build upon.

This is the first textbook dedicated to explaining how artificial intelligence (AI) techniques can be used in and for games. After introductory chapters that explain the background and key techniques in AI and games, the authors explain how to use AI to play games, to generate content for games and to model players. The book will be suitable for undergraduate and graduate courses in games, artificial intelligence, design, human-computer interaction, and computational intelligence, and also for self-study by industrial game developers and practitioners. The authors have developed a website (http://www.gameaibook.org) that complements the material covered in the book with up-to-date exercises, lecture slides and reading.

Leverage the power of the Unity 2018 game engine to create games with brilliant gameplay and high replayability. Key Features Develop different types of games from scratch with Unity 2018 Discover the secrets of creating AAA quality shaders without writing long algorithms Script intelligent game characters and agents using Artificial Intelligence techniques and algorithms Book Description Through this Learning Path, you'll learn how to leverage the features of Unity and create amazing games, ranging from action shooters and mind-bending puzzle games to adventure and Virtual Reality(VR) games. If you have no prior experience of using Unity, you can still benefit from this Learning Path, which easily explains the complete working of the Unity toolset. You'll start by learning how to create compelling shaders using Unity and understanding everything you need to know about vectors. This includes useful inputs on how lighting is constructed with vectors and how textures are used to create complex effects without the need for advanced math. In the succeeding chapters, you'll also be able to use popular AI techniques, such as A* and A*mbush to develop intelligent pathfinding agents for your games. The book will also guide you through different algorithms for creating decision-making agents that go beyond simple behaviors and movement. By the end of this Learning Path, you will have developed all the basic skills to create highly engaging and replayable games. This Learning Path includes content from the following Packt products: Unity 2018 By Example - Second Edition by Alan Thorn Unity 2018 Shaders and Effects Cookbook - Third Edition by John P. Doran, Alan Zucconi Unity 2018 Artificial Intelligence Cookbook - Second Edition by Jorge Palacios What you will learn Understand concepts such as game objects, components, and scenes Create functional games with C# scripting Write shaders from scratch in ShaderLab and HLSL/Cg Develop intelligent pathfinding agents with A and Ambush Work with terrains and world-creation tools Simulate senses for agents to make decisions based on the environment Implement waypoints by creating a manual selector Enhance games with volumetric explosions, special effects, and visuals Who this book is for If you are a game developer who wants to learn tools that can transform your gameplay, this beginner-level Learning Path is ideal for you. Having basic knowledge of C# will help you grasp the concepts explained in the book easily.

Transform games into environments using machine learning and Deep learning with Tensorflow, Keras, and Unity Key Features Learn how to apply core machine learning concepts to your games with Unity Learn the Fundamentals of Reinforcement Learning and Q-Learning and apply them to your games Learn How to build multiple asynchronous agents and run them in a training scenario Book Description Unity Machine Learning agents allow researchers and developers to create games and simulations using the Unity Editor, which serves as an environment where intelligent agents can be trained with machine learning methods through a simple-to-use Python API. This book takes you from the basics of Reinforcement and Q Learning to building Deep Recurrent Q-Network agents that cooperate or compete in a multi-agent ecosystem. You will start with the basics of Reinforcement Learning and how to apply it to problems. Then you will learn how to build self-learning advanced neural networks with Python and Keras/TensorFlow. From there you move o n to more advanced training scenarios where you will learn further innovative ways to train your network with A3C, imitation, and curriculum learning models. By the end of the book, you will have learned how to build more complex environments by building a cooperative and competitive multi-agent ecosystem. What you will learn Develop Reinforcement and Deep Reinforcement Learning for games. Understand complex and advanced concepts of reinforcement learning and neural networks Explore various training strategies for cooperative and competitive agent development Adapt the basic script components of Academy, Agent, and Brain to be used with Q Learning. Enhance the Q Learning model with improved training strategies such as Greedy-Epsilon exploration Implement a simple NN with Keras and use it as an external brain in Unity Understand how to add LTSM blocks to an existing DQN Build multiple asynchronous agents and run them in a training scenario Who this book is for This book is intended for developers with an interest in using Machine learning algorithms to develop better games and simulations with Unity. The reader will be required to have a working knowledge of C# and a basic understanding of Python.

Jump into the world of Game AI development About This Book Move beyond using libraries to create smart game AI, and create your own AI projects from scratch Implement the latest algorithms for AI development and in-game interaction Customize your existing game AI and make it better and more efficient to improve your overall game performance Who This Book Is For This book is for game developers with a basic knowledge of game development techniques and some basic programming techniques in C# or C++. What You Will Learn Get to know the basics of how to create different AI for different type of games Know what to do when something interferes with the AI choices and how the AI should behave if that happens Plan the interaction between the AI character and the environment using Smart Zones or Triggering Events Use animations correctly, blending one animation into another and rather than stopping one animation and starting another Calculate the best options for the AI to move using Pruning Strategies, Wall Distances, Map Preprocess Implementation, and Forced Neighbours Create Theta algorithms to the AI to find short and realistic looking paths Add many characters into the same scene and make them behave like a realistic crowd In Detail The book starts with the basics examples of AI for different game genres and directly jumps into defining the probabilities and possibilities of the AI character to determine character movement. Next, you'll learn how AI characters should behave within the environment created. Moving on, you'll explore how to work with animations. You'll also plan and create pruning strategies, and create Theta algorithms to find short and realistic looking game paths. Next, you'll learn how the AI should behave when there is a lot of characters in the same scene. You'll explore which methods and algorithms, such as possibility maps, Forward Chaining Plan, Rete Algorithm, Pruning Strategies, Wall Distances, and Map Preprocess Implementation should be used on different occasions. You'll discover how to overcome some limitations, and how to deliver a better experience to the player. By the end of the book, you think differently about AI. Style and approach The book has a step-by-step tutorial style approach. The algorithms are explained by implementing them in #.