Machine Learning For Real Time Strategy Computer Games
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Machine Learning For Real Time Strategy Computer Games
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Author : Warren Marusiak
language : en
Publisher:
Release Date : 2008
Machine Learning For Real Time Strategy Computer Games written by Warren Marusiak and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Artificial intelligence categories.
A commercial Real-Time Strategy (RTS) game requires an artificial intelligence component, simply called an "AI", capable of providing a human player with a challenging opponent. These AIs must simulate the play style of a competent human player. The AIs of current RTS games can play with multiple levels of skill. However, their simulation of a competent human player is incomplete because they cannot change their tactics to adapt to a human player.
Artificial Intelligence Video Games
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Author : Fouad Sabry
language : en
Publisher: One Billion Knowledgeable
Release Date : 2023-07-04
Artificial Intelligence Video Games written by Fouad Sabry and has been published by One Billion Knowledgeable this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-04 with Computers categories.
What Is Artificial Intelligence Video Games Artificial intelligence (AI) is used in video games to develop responsive, adaptive, or intelligent behaviors, primarily in non-player characters (NPCs), that are akin to the intellect of humans. Since the beginning of the video game industry in the 1950s, artificial intelligence has been an essential component of the medium. Artificial intelligence (AI) in video games is a discrete topic that is distinct from AI in academic settings. Rather than serving the purposes of machine learning or decision making, it is designed to enhance the experience of game players. The concept of artificial intelligence (AI) opponents became very popular during the golden age of arcade video games. This concept manifested itself in the form of graduated difficulty levels, distinct movement patterns, and in-game events that were reliant on the player's input. The behavior of non-player characters (NPCs) in modern games is frequently governed by tried-and-true methods such as pathfinding and decision trees. Data mining and procedural content production are two examples of AI applications that are frequently utilized in methods that are not immediately obvious to the user. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Artificial intelligence in video games Chapter 2: Artificial intelligence Chapter 3: List of artificial intelligence projects Chapter 4: Video game programmer Chapter 5: Interactive storytelling Chapter 6: Outline of video games Chapter 7: Outline of artificial intelligence Chapter 8: General game playing Chapter 9: Dynamic game difficulty balancing Chapter 10: Machine learning in video games (II) Answering the public top questions about artificial intelligence video games. (III) Real world examples for the usage of artificial intelligence video games in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of artificial intelligence video games' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of artificial intelligence video games.
Deep Learning In Gaming And Animations
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Author : Vikas Chaudhary
language : en
Publisher: CRC Press
Release Date : 2021-12-07
Deep Learning In Gaming And Animations written by Vikas Chaudhary and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-07 with Computers categories.
Over the last decade, progress in deep learning has had a profound and transformational effect on many complex problems, including speech recognition, machine translation, natural language understanding, and computer vision. As a result, computers can now achieve human-competitive performance in a wide range of perception and recognition tasks. Many of these systems are now available to the programmer via a range of so-called cognitive services. More recently, deep reinforcement learning has achieved ground-breaking success in several complex challenges. This book makes an enormous contribution to this beautiful, vibrant area of study: an area that is developing rapidly both in breadth and depth. Deep learning can cope with a broader range of tasks (and perform those tasks to increasing levels of excellence). This book lays a good foundation for the core concepts and principles of deep learning in gaming and animation, walking you through the fundamental ideas with expert ease. This book progresses in a step-by-step manner. It reinforces theory with a full-fledged pedagogy designed to enhance students' understanding and offer them a practical insight into its applications. Also, some chapters introduce and cover novel ideas about how artificial intelligence (AI), deep learning, and machine learning have changed the world in gaming and animation. It gives us the idea that AI can also be applied in gaming, and there are limited textbooks in this area. This book comprehensively addresses all the aspects of AI and deep learning in gaming. Also, each chapter follows a similar structure so that students, teachers, and industry experts can orientate themselves within the text. There are few books in the field of gaming using AI. Deep Learning in Gaming and Animations teaches you how to apply the power of deep learning to build complex reasoning tasks. After being exposed to the foundations of machine and deep learning, you will use Python to build a bot and then teach it the game's rules. This book also focuses on how different technologies have revolutionized gaming and animation with various illustrations.
Encyclopedia Of Computer Graphics And Games
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Author : Newton Lee
language : en
Publisher: Springer Nature
Release Date : 2024-01-19
Encyclopedia Of Computer Graphics And Games written by Newton Lee and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-19 with Computers categories.
Encyclopedia of Computer Graphics and Games (ECGG) is a unique reference resource tailored to meet the needs of research and applications for industry professionals and academic communities worldwide. The ECGG covers the history, technologies, and trends of computer graphics and games. Editor Newton Lee, Institute for Education, Research, and Scholarships, Los Angeles, CA, USA Academic Co-Chairs Shlomo Dubnov, Department of Music and Computer Science and Engineering, University of California San Diego, San Diego, CA, USA Patrick C. K. Hung, University of Ontario Institute of Technology, Oshawa, ON, Canada Jaci Lee Lederman, Vincennes University, Vincennes, IN, USA Industry Co-Chairs Shuichi Kurabayashi, Cygames, Inc. & Keio University, Kanagawa, Japan Xiaomao Wu, Gritworld GmbH, Frankfurt am Main, Hessen, Germany Editorial Board Members Leigh Achterbosch, School of Science, Engineering, IT and Physical Sciences, Federation University Australia Mt Helen, Ballarat, VIC, Australia Ramazan S. Aygun, Department of Computer Science, Kennesaw State University, Marietta, GA, USA Barbaros Bostan, BUG Game Lab, Bahçeşehir University (BAU), Istanbul, Turkey Anthony L. Brooks, Aalborg University, Aalborg, Denmark Guven Catak, BUG Game Lab, Bahçeşehir University (BAU), Istanbul, Turkey Alvin Kok Chuen Chan, Cambridge Corporate University, Lucerne, Switzerland Anirban Chowdhury, Department of User Experience and Interaction Design, School of Design (SoD), University of Petroleum and Energy Studies (UPES), Dehradun, Uttarakhand, India Saverio Debernardis, Dipartimento di Meccanica, Matematica e Management, Politecnico di Bari, Bari, Italy Abdennour El Rhalibi, Liverpool John Moores University, Liverpool, UK Stefano Ferretti, Department of Computer Science and Engineering, University of Bologna, Bologna, Italy Han Hu, School of Information and Electronics, Beijing Institute of Technology, Beijing, China Ms. Susan Johnston, Select Services Films Inc., Los Angeles, CA, USA Chris Joslin, Carleton University, Ottawa, Canada Sicilia Ferreira Judice, Department of Computer Science, University of Calgary, Calgary, Canada Hoshang Kolivand, Department Computer Science, Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool, UK Dario Maggiorini, Department of Computer Science, University of Milan, Milan, Italy Tim McGraw, Purdue University, West Lafayette, IN, USA George Papagiannakis, ORamaVR S.A., Heraklion, Greece; FORTH-ICS, Heraklion Greece University of Crete, Heraklion, Greece Florian Richoux, Nantes Atlantic Computer Science Laboratory (LINA), Université de Nantes, Nantes, France Andrea Sanna, Dipartimento di Automatica e Informatica, Politecnico di Torino, Turin, Italy Yann Savoye, Institut fur Informatik, Innsbruck University, Innsbruck, Austria Sercan Şengün, Wonsook Kim School of Art, Illinois State University, Normal, IL, USA Ruck Thawonmas, Ritsumeikan University, Shiga, Japan Vinesh Thiruchelvam, Asia Pacific University of Technology & Innovation, Kuala Lumpur, Malaysia Rojin Vishkaie, Amazon, Seattle, WA, USA Duncan A. H. Williams, Digital Creativity Labs, Department of Computer Science, University of York, York, UK Sai-Keung Wong, National Chiao Tung University, Hsinchu, Taiwan Editorial Board Intern Sam Romershausen, Vincennes University, Vincennes, IN, USA
A Multi Layer Case Based Reinforcement Learning Approach To Adaptive Tactical Real Time Strategy Game Ai
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Author : Stefan Wender
language : en
Publisher:
Release Date : 2015
A Multi Layer Case Based Reinforcement Learning Approach To Adaptive Tactical Real Time Strategy Game Ai written by Stefan Wender and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Artificial intelligence categories.
Real-time-strategy (RTS) games offer complex environments that exhibit many interesting problems for research in artificial intelligence (AI). However, research into machine learning (ML) approaches in this domain often focuses on small sub-problems. Architectures that address multiple tasks are often patchworks of different non-adaptive components. This thesis investigates and aims to advance research into adaptive ML approaches to RTS game AI. The thesis also focuses on creating novel techniques and applications for acquiring knowledge through online learning in RTS environments, specifically their tactical and reactive tasks. As a first step toward an adaptive ML approach, reinforcement learning (RL), a machine learning technique that works well in unknown environments, is evaluated for its performance in the RTS game domain. A number of RL algorithms are compared against each other for their performance in small-scale combat in the commercial RTS game StarCraft. The best-performing Q-learning algorithm is used in the subsequent creation of other ML modules. RL is combined with case-based reasoning (CBR) into a hybrid approach that is able to address more complex problems through generalizing over large state-action-spaces. This hybrid module is found to increase speed of convergence when compared to simple table-based RL. Furthermore, this step includes in-depth evaluation of case-base behaviour and the optimisation of algorithmic parameters for future use. Given the complexity inherent in the RTS domain, a hierarchical decomposition of the problem is proposed which subdivides the problem space in order to address the overall task of micromanaging combat units in RTS games. Based on this decomposition and inspired by other hierarchical paradigms such as layered learning (LL) and hierarchical CBR, a hierarchical architecture is created. That hierarchical architecture includes individual components for RTS game tasks which fall into the tactical and reactive layers. These tasks are Navigation, Formation, Attack , Retreat, and, on the highest level, Tactical Unit Selection. For each of these tasks, a separate module is created, on many occasions using combinations of CBR and RL to acquire and manage the knowledge required to solve the particular task. Each module is individually evaluated to successfully solve its assigned problem. Finally, all modules are integrated in a three-tier layered implementation and are shown to successfully solve tactical and reactive tasks in various evaluation scenarios.
Artificial Intelligence And Games
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Author : Georgios N. Yannakakis
language : en
Publisher: Springer
Release Date : 2018-02-17
Artificial Intelligence And Games written by Georgios N. Yannakakis and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-17 with Computers categories.
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.
General Game Playing
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Author : Fouad Sabry
language : en
Publisher: One Billion Knowledgeable
Release Date : 2023-07-04
General Game Playing written by Fouad Sabry and has been published by One Billion Knowledgeable this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-04 with Computers categories.
What Is General Game Playing The concept of general game playing, sometimes known as GGP, refers to the development of artificial intelligence programs that are capable of competing well in more than one game. Computers are programmed to play many different games, such as chess, using an algorithm that is built specifically for that game and cannot be used in any other setting. For instance, a computer software that is designed to play chess cannot also play checkers. On the road to creating artificial general intelligence, generic game playing is seen as a necessary milestone. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: General game playing Chapter 2: Artificial intelligence Chapter 3: Machine learning Chapter 4: Game Description Language Chapter 5: List of programming languages for artificial intelligence Chapter 6: Monte Carlo tree search Chapter 7: Deep reinforcement learning Chapter 8: Artificial intelligence in video games Chapter 9: Machine learning in video games Chapter 10: Google DeepMind (II) Answering the public top questions about general game playing. (III) Real world examples for the usage of general game playing in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of general game playing' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of general game playing.
Artificial Intelligence For Computer Games
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Author : Pedro Antonio González-Calero
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-03-01
Artificial Intelligence For Computer Games written by Pedro Antonio González-Calero and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-01 with Technology & Engineering categories.
The book presents some of the most relevant results from academia in the area of Artificial Intelligence for games. It emphasizes well theoretically supported work supported by developed prototypes, which should lead into integration of academic AI techniques into current electronic entertainment games. The book elaborates on the main results produced in Academia within the last 10 years regarding all aspects of Artificial Intelligence for games, including pathfinding, decision making, and learning. A general theme of the book is the coverage of techniques for facilitating the construction of flexible not prescripted AI for agents in games. Regarding pathfinding, the book includes new techniques for implementing real-time search methods that improve the results obtained through AI, as well as techniques for learning pathfinding behavior by observing actual players. Regarding decision making, the book describes new techniques for authoring tools that facilitate the construction by game designers (typically nonprogrammers) of behavior controlling software, by reusing patterns or actual cases of past behavior. Additionally, the book will cover a number of approaches proposed for extending the essentially pre-scripted nature of current commercial videogames AI into a more interactive form of narrative, where the story emerges from the interaction with the player. Some of those approaches rely on a layered architecture for the character AI, including beliefs, intentions and emotions, taking ideas from research on agent systems. The book also includes chapters on techniques for automatically or semiautomatically learning complex behavior from recorded traces of human or automatic players using different combinations of reinforcement learning, case-based reasoning, neural networks and genetic algorithms.
Ai For Games
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Author : Ian Millington
language : en
Publisher: CRC Press
Release Date : 2021-11-16
Ai For Games written by Ian Millington and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-16 with Computers categories.
What is artificial intelligence? How is artificial intelligence used in game development? Game development lives in its own technical world. It has its own idioms, skills, and challenges. That’s one of the reasons games are so much fun to work on. Each game has its own rules, its own aesthetic, and its own trade-offs, and the hardware it will run on keeps changing. AI for Games is designed to help you understand one element of game development: artificial intelligence (AI).
Component Action Deep Q Learning For Real Time Strategy Game Ai
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Author : Richard Kelly
language : en
Publisher:
Release Date : 2021
Component Action Deep Q Learning For Real Time Strategy Game Ai written by Richard Kelly and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Real-time Strategy (RTS) games provide a challenging environment for AI research, due to their large state and action spaces, hidden information, and real-time gameplay. The RTS game StarCraft II has become a new test-bed for deep reinforcement learning (RL) systems using the StarCraft II Learning Environment (SC2LE). Recently the full game of StarCraft II has been approached with a complex multi-agent RL system only possible with extremely large financial investments. In this thesis we will describe existing work in RTS AI and motivate our work adapting the deep Q-learning (DQN) RL algorithm to accommodate the multi-dimensional action-space of the SC2LE. We then present the results of our experiments using custom combat scenarios. First, we compare methods for calculating DQN training loss with action components. Second, we show that policies trained with component-action DQN for five hours perform comparably to scripted policies in smaller scenarios and outperform them in larger scenarios. Third, we explore several ways to transfer policies between scenarios, and show that it is a viable method to reduce training time. We show that policies trained on scenarios with fewer units can be applied to larger scenarios and to scenarios with different unit types with only a small loss in performance.