Automatic Graphics And Game Content Generation Through Evolutionary Computation

Simulation and game content includes the levels, models, textures, items, and other objects encountered and possessed by players during the game. In most modern video games and simulation software, the set of content shipped with the product is static and unchanging, or at best, randomized within a narrow set of parameters. However, ideally, if game content could be constantly and automatically renewed, players would remain engaged longer in the evolving stream of content. This dissertation introduces three novel technologies that together realize this ambition. (1) The first, NEAT Particles, is an evolutionary method to enable users to quickly and easily create complex particle effects through a simple interactive evolutionary computation (IEC) interface. That way, particle effects become an evolvable class of content, which is exploited in the remainder of the dissertation. In particular, (2) a new algorithm called content-generating NeuroEvolution of Augmenting Topologies (cgNEAT) is introduced that automatically generates graphical and game content while the game is played, based on the past preferences of the players. Through cgNEAT, the game platform on its own can generate novel content that is designed to satisfy its players. Finally, (3) the Galactic Arms Race (GAR) multiplayer online video game is constructed to demonstrate these techniques working on a real online gaming platform. In GAR, which was made available to the public and playable online, players pilot space ships and fight enemies to acquire unique particle system weapons that are automatically evolved by the cgNEAT algorithm. The resulting study shows that cgNEAT indeed enables players to discover a wide variety of appealing content that is not only novel, but also based on and extended from previous content that they preferred in the past. The implication is that with cgNEAT it is now possible to create applications that generate their own content to satisfy users, potentially significantly reducing the cost of content creation and considerably increasing entertainment value with a constant stream of evolving content

A spatially-structured PCG method for content diversity in a Physics-based simulation game

This paper presents a spatially-structured evolutionary algorithm (EA) to procedurally generate game maps of di ferent levels of di ficulty to be solved, in Gravityvolve!, a physics-based simulation videogame that we have implemented and which is inspired by the n- body problem, a classical problem in the fi eld of physics and mathematics. The proposal consists of a steady-state EA whose population is partitioned into three groups according to the di ficulty of the generated content (hard, medium or easy) which can be easily adapted to handle the automatic creation of content of diverse nature in other games. In addition, we present three fitness functions, based on multiple criteria (i.e:, intersections, gravitational acceleration and simulations), that were used experimentally to conduct the search process for creating a database of maps with di ferent di ficulty in Gravityvolve!.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Interactive genetic engineering of evolved video game content

Procedural content generation techniques can increase replayability and lower the burden on developers by satisfying players\u27 demand for new content. However, procedural content also creates an opportunity for new kinds of player-driven content customization by giving players access to the parameterized content space. This paper presents such a technique that enables players to manually customize evolved content represented by artificial neural networks. In particular, particle system weapons evolved by the multiplayer space shooter called Galactic Arms Race (GAR) can be further genetically engineered by the players themselves in a new extension to the game called the Weapons Lab. Results are presented that demonstrate procedural weapons evolved by the game that are further customized by players in the Weapons Lab. The implication is that procedurallygenerated content of many types can also be customized by players, adding a further dimension to its potential appeal. Copyright 2010 ACM

Evolutionary Machine Learning and Games

Evolutionary machine learning (EML) has been applied to games in multiple ways, and for multiple different purposes. Importantly, AI research in games is not only about playing games; it is also about generating game content, modeling players, and many other applications. Many of these applications pose interesting problems for EML. We will structure this chapter on EML for games based on whether evolution is used to augment machine learning (ML) or ML is used to augment evolution. For completeness, we also briefly discuss the usage of ML and evolution separately in games.Comment: 27 pages, 5 figures, part of Evolutionary Machine Learning Book (https://link.springer.com/book/10.1007/978-981-99-3814-8

Towards procedural strategy game generation : evolving complementary unit types

The Strategy Game Description Game Language (SGDL) is intended to become a complete description of all aspects of strategy games, including rules, parameters, scenarios, maps, and unit types. One of the main envisioned uses of SGDL, in combination with an evolutionary algorithm and appropriate fitness functions, is to allow the generation of complete new strategy games or variations of old ones. This paper presents a first version of SGDL, capable of describing unit types and their properties, together with plans for how it will be extended to other sub-domains of strategy games. As a proof of the viability of the idea and implementation, an experiment is presented where unit types are evolved so as to generate complementary properties. A fitness function based on Monte Carlo simulation of gameplay is devised to test complementarity.peer-reviewe

Neuroevolution in Games: State of the Art and Open Challenges

This paper surveys research on applying neuroevolution (NE) to games. In neuroevolution, artificial neural networks are trained through evolutionary algorithms, taking inspiration from the way biological brains evolved. We analyse the application of NE in games along five different axes, which are the role NE is chosen to play in a game, the different types of neural networks used, the way these networks are evolved, how the fitness is determined and what type of input the network receives. The article also highlights important open research challenges in the field.Comment: - Added more references - Corrected typos - Added an overview table (Table 1

Deep learning for video game playing

In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards

Using genetic algorithms for real-time dynamic difficulty adjustment in games

Dynamic Difficulty Adjustment is the area of research that seeks ways to balance game difficulty with challenge, making it an engaging experience for all types of players, from novice to veteran, without making it frustrating or boring. In this dissertation we propose an approach that aims to evolve agents, in this case predators, as a group and in real time, in a way that they adapt to a changing environment. We showcase our approach after using a generic genetic algorithm in two scenarios, pitting the predators vs passive prey in one scenario and pitting the predators vs aggressive prey in another, this is done to create a basis for our approach and then test our algorithm in four different scenarios, the first two are the same as the generic genetic algorithm and in the next two we switch prey in the middle of the experience progressively from passive to aggressive or vice versa.Adaptação Dinâmica de Dificuldade é a área de pesquisa que procura formas de equilibrar a dificuldade do jogo com o desafio, tornando-o uma experiência envolvente para todos os tipos de jogadores, desde principiantes a veteranos, sem o tornar frustrante ou aborrecido. Nesta dissertação propomos uma abordagem que visa evoluir os agentes, neste caso predadores, como um grupo e em tempo real, de forma a que estes se adaptem a um ambiente em mudança. Nós mostramos a nossa abordagem depois de usar um algoritmo genético genérico em dois cenários, colocando os predadores versus presas passivas num cenário e colocando os predadores versus presas agressivas noutro, isto é feito para criar uma base para a nossa abordagem e depois testamos o nosso algoritmo em quatro cenários diferentes, os dois primeiros são os mesmos que o algoritmo genético genérico e nos dois seguintes trocamos as presas a meio da experiência progressivamente de passivas para agressivas ou vice-versa