Synthesis Lectures on Games and Computational Intelligence – serie

This book is a formalization of collected notes from an introductory game theory course taught at Queen's University. Classical games, like the Prisoner's Dilemma and the Lady and the Tiger, are joined by a procedure for transforming mathematical games into card games.

Evolving agents to play games is a promising technology. The book also provides a proposal for an agent training protocol that is intended as a step toward being able to train humaniform agents—in other words, agents that plausibly model human behavior.

Automatic content generation is the production of content for games, web pages, or other purposes by procedural means. Search-based automatic content generation employs search-based algorithms to accomplish automatic content generation. This book presents a number of different techniques for search-based automatic content generation where the search algorithm is an evolutionary algorithm. The chapters treat puzzle design, the creation of small maps or mazes, the use of L-systems and a generalization of L-system to create terrain maps, the use of cellular automata to create maps, and, finally, the decomposition of the design problem for large, complex maps culminating in the creation of a map for a fantasy game module with designersupplied content and tactical features.The evolutionary algorithms used for the different types of content are generic and similar, with the exception of the novel sparse initialization technique are presented in Chapter 2. The points where the content generation systems vary are in the design of their fitness functions and in the way the space of objects being searched is represented. A large variety of different fitness functions are designed and explained, and similarly radically different representations are applied to the design of digital objects all of which are, essentially, maps for use in games.

A basic introduction to game theory is provided with an emphasis on the prisoner's dilemma, the most widely studied social dilemma game.

Research on general video game playing aims at designing agents or content generators that can perform well in multiple video games, possibly without knowing the game in advance and with little to no specific domain knowledge.

The book details topics to reflect on possible emotional outcomes of the player at the early stages of game design as well as the methodology for presenting questions to players in such a way as to elicit authentic feedback.This book is intended mainly for game designers, researchers, and developers.

This book surveys current and future approaches to generating video game content with machine learning or Procedural Content Generation via Machine Learning (PCGML).  Machine learning is having a major impact on many industries, including the video game industry.  PCGML addresses the use of computers to generate new types of content for video games (game levels, quests, characters, etc.) by learning from existing content.  The authors illustrate how PCGML is poised to transform the video games industry and provide the first ever beginner-focused guide to PCGML.  This book features an accessible introduction to machine learning topics, and readers will gain a broad understanding of currently employed PCGML approaches in academia and industry.  The authors provide guidance on how best to set up a PCGML project and identify open problems appropriate for a research project or thesis.  This book is written with machine learning and games novices in mind and includes discussions of practical and ethical considerations along with resources and guidance for starting a new PCGML project.

This second edition updates and expands upon the first beginner-focused guide to Procedural Content Generation via Machine Learning (PCGML), which is the use of computers to generate new types of content for video games (game levels, quests, characters, etc.) by learning from existing content.