Co-operative coevolution for computational creativity: a case study In videogame design

The term procedural content generation (PCG) refers to writing software which can synthesise content for a game (or other media such as film) without further intervention from a designer. PCG has become a rich area of research in recent years, finding new ways to apply artificial intelligence to generate high-quality game content such as levels, weapons or puzzles for games. Such research is generally constrained to a single type of content, however, with the assumption that the remainder of the game's design will be fixed by an external designer. Generating many aspects of a game's design simultaneously, perhaps ultimately generating the entirety of a game's design, using PCG is not a well-explored idea. The notion of automated game design is not well-established, and is not seen as a task distinct from simply performing lots of PCG tasks at the same time. In particular, the high-level design tasks guiding the creative direction of a game are all but completely absent in PCG literature, because it is rare that a designer wishes to hand over such responsibility to a PCG system. We present here ANGELINA, an automated game designer that has developed games using a multi-faceted approach to content generation underpinned by a co-operative co-evolutionary approach which breaks down a game design into several distinct tasks, each of which controlled by an evolutionary subsystem within ANGELINA. We will show that this approach works well to automate game design, can be ported across many game engines and game genres, and can be enhanced and extended using novel computational creativity techniques to give the system a heightened sense of autonomy and independence.Open Acces

3D terrain generation using neural networks

With the increase in computation power, coupled with the advancements in the field in the form of GANs and cGANs, Neural Networks have become an attractive proposition for content generation. This opened opportunities for Procedural Content Generation algorithms (PCG) to tap Neural Networks generative power to create tools that allow developers to remove part of creative and developmental burden imposed throughout the gaming industry, be it from investors looking for a return on their investment and from consumers that want more and better content, fast. This dissertation sets out to develop a PCG mixed-initiative tool, leveraging cGANs, to create authored 3D terrains, allowing users to directly influence the resulting generated content without the need for formal training on terrain generation or complex interactions with the tool to influence the generative output, as opposed to state of the art generative algorithms that only allow for random content generation or are needlessly complex. Testing done to 113 people online, as well as in-person testing done to 30 people, revealed that it is indeed possible to develop a tool that allows users from any level of terrain creation knowledge, and minimal tool training, to easily create a 3D terrain that is more realistic looking than those generated by state-of-the-art solutions such as Perlin Noise.Com o aumento do poder de computação, juntamente com os avanços neste campo na forma de GANs e cGANs, as Redes Neurais tornaram-se numa proposta atrativa para a geração de conteúdos. Graças a estes avanços, abriram-se oportunidades para os algoritmos de Geração de Conteúdos Procedimentais(PCG) explorarem o poder generativo das Redes Neurais para a criação de ferramentas que permitam aos programadores remover parte da carga criativa e de desenvolvimento imposta em toda a indústria dos jogos, seja por parte dos investidores que procuram um retorno do seu investimento ou por parte dos consumidores que querem mais e melhor conteúdo, o mais rápido possível. Esta dissertação pretende desenvolver uma ferramenta de iniciativa mista PCG, alavancando cGANs, para criar terrenos 3D cocriados, permitindo aos utilizadores influenciarem diretamente o conteúdo gerado sem necessidade de terem formação formal sobre a criação de terrenos 3D ou interações complexas com a ferramenta para influenciar a produção generativa, opondo-se assim a algoritmos generativos comummente utilizados, que apenas permitem a geração de conteúdo aleatório ou que são desnecessariamente complexos. Um conjunto de testes feitos a 113 pessoas online e a 30 pessoas presencialmente, revelaram que é de facto possível desenvolver uma ferramenta que permita aos utilizadores, de qualquer nível de conhecimento sobre criação de terrenos, e com uma formação mínima na ferramenta, criar um terreno 3D mais realista do que os terrenos gerados a partir da solução de estado da arte, como o Perlin Noise, e de uma forma fácil

Playing for Whom? Understanding Empathy and Trauma in Postcolonial and Global South Videogame Narratives through Witnessing

This project seeks to better understand representations of and interactions with postcolonial and global south identities via trauma narratives as they are encountered in new media, particularly videogames. Building on previous research on interactive media and narratological structure both in and out of trauma studies, I investigate the ways of representing trauma narratives within videogames, challenge existing models of understanding trauma narratives in videogames by bringing postcolonial and global south narratives into consideration, and present a different model and method of analysis for such narratives based on prior media that considers the ethics of rhetorical engagement with the content and the medium

Languages of games and play: A systematic mapping study

Digital games are a powerful means for creating enticing, beautiful, educational, and often highly addictive interactive experiences that impact the lives of billions of players worldwide. We explore what informs the design and construction of good games to learn how to speed-up game development. In particular, we study to what extent languages, notations, patterns, and tools, can offer experts theoretical foundations, systematic techniques, and practical solutions they need to raise their productivity and improve the quality of games and play. Despite the growing number of publications on this topic there is currently no overview describing the state-of-the-art that relates research areas, goals, and applications. As a result, efforts and successes are often one-off, lessons learned go overlooked, language reuse remains minimal, and opportunities for collaboration and synergy are lost. We present a systematic map that identifies relevant publications and gives an overview of research areas and publication venues. In addition, we categorize research perspectives along common objectives, techniques, and approaches, illustrated by summaries of selected languages. Finally, we distill challenges and opportunities for future research and development

Probabilistic Modeling for Game Content Creation and Adaption

Dynamic Difficulty Adjustment studies how games can adapt content totheir users’ skill level, aiming to keep them in flow. Most of these methodsmaximize engagement or minimize churn by adapting factors like the opponentAI or the availability of resources. However, such methods do notmaintain a model of the player, and use technologies that are highly specificto the games in which they are tested (e.g. requiring forward modelsfor enemy AIs based on planning agents). Designers may also intend tofind content that is more difficult/easier on purpose, and current methodsdo not allow for such targeting.This thesis proposes and tests a framework for adapting game content tousers based on Bayesian Optimization, giving designers flexibility whenchoosing which skill level to target. Starting with a design space, a metricto be measured, a prior over this metric, and a target value, our frameworkquickly searches possible levels/tasks for one with ideal difficulty (i.e. closeto the specified target). In the process, our framework maintains a simpledata-driven model of the player, which could be used for further decisionmakingand analysis.We test this framework in two settings: adapting content to planning agentsbased on search algorithms likeMonte Carlo Tree Search and Rolling HorizonEvolution in a dungeon crawler-type game, and adapting both Sudokupuzzles and dungeon crawler levels to players. Our framework successfullyadapts content to planning agents as long as their skill level is not extreme,and takes roughly 7 iterations to find an appropriate Sudoku puzzle.Additionally, instead of relying on designers to specify a real-valued encodingof the content (e.g. the number of pre-filled cells in a Sudoku puzzle),we investigate learning this encoding automatically usingDeep GenerativeModels. In other words, we explore design spaces learned as latent spacesof Variational Autoencoders using tile-based representations of games likeSuperMario Bros and The Legend of Zelda.Our final contribution is a novel way of interpolating, sampling and optimizingin the playable regions of latent spaces of Variational Autoencoders,and addresses the challenge that generative models are not always guaranteedto decode playable content. This contribution, based on differentialgeometry, is inspired by recent advancements in domains like robotics andproteinmodeling. We combine these ideas of safe generation with contentoptimization and propose a restricted version of Bayesian Optimization,which optimizes content inside playable regions. We see a clear trade-off:restricting the latent space to playable regions decreases the diversity ofthe generated content, as well as the quality of the optimal values in theoptimization.In summary, this thesis studies applications of Bayesian Optimization andDeep Generative Models to the problem of creating and adapting gamecontent to users. We develop a framework that quickly finds relevant levelsin settings varying from corpora of levels to the latent spaces of generativemodels, and we show in experiments involving both human and artificialplayers that this framework finds appropriate game content in a few iterations.This framework is readily applicable, and could be used to creategames that learn and adapt to their players.<br/