Online Level Generation in Super Mario Bros via Learning Constructive Primitives

n procedural content generation (PCG), how to assure the quality of procedural games and how to provide effective control for designers are two major challenges. To tackle these issues, this paper exploits the synergy between rule-based and learning-based methods to produce quality yet controllable game segments in Super Mario Bros (SMB), hereinafter named constructive primitives (CPs). Easy-to-design rules are employed for removal of apparently unappealing game segments, and subsequent data-driven quality evaluation function is learned based on designer's annotations to deal with more complicated quality issues. The learned CPs provide not only quality game segments but also an effective control manner at a local level for designers. As a result, a complete quality game level can be generated online by integrating relevant constructive primitives via controllable parameters. Extensive simulation results demonstrate that the proposed approach efficiently generates controllable yet quality game levels in terms of different quality measures

Online Game Level Generation from Music

Game consists of multiple types of content, while the harmony of different content types play an essential role in game design. However, most works on procedural content generation consider only one type of content at a time. In this paper, we propose and formulate online level generation from music, in a way of matching a level feature to a music feature in real-time, while adapting to players' play speed. A generic framework named online player-adaptive procedural content generation via reinforcement learning, OPARL for short, is built upon the experience-driven reinforcement learning and controllable reinforcement learning, to enable online level generation from music. Furthermore, a novel control policy based on local search and k-nearest neighbours is proposed and integrated into OPARL to control the level generator considering the play data collected online. Results of simulation-based experiments show that our implementation of OPARL is competent to generate playable levels with difficulty degree matched to the ``energy'' dynamic of music for different artificial players in an online fashion

O uso de IA para a criação procedural de conteúdo espacial em jogos

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Sistemas de Informação.A medida que o mercado de jogos cresce, a competição entre os desenvolvedores e as exigências dos consumidores também sobe. O custo por hora de um time de desenvolvimento pode tornar um jogo proibitivamente caro para muitas empresas e as demandas dos consumidores por mais conteúdo por menos dinheiro fazem grande pressão. Para solucionar a demanda competitiva e de mercado, pode ser de interesse a automatização da produção de conteúdo. Por meio de estudos e técnicas de IA, é possível a produção procedural de ambientes de qualidade para uma grande variedade de jogos, poupando tempo de desenvolvimento ou garantindo ao jogador maior variedade de experiências. Tendo isso em mente, foi realizado um levantamento de diferentes técnicas utilizaveis na criação procedural de espaços em jogos digitais. Para demonstrar como estas podem ser colocadas em prática, foi implementado um Algoritmo Evolutivo que realiza a criação procedural de conteúdo espacial no jogo Infinite Mario Bros, gerando fases de qualidade e com parâmetros controláveis.As the games industry grows, so too does the competition between developers and the expectations from the customer base. The developing costs for a team of full time developers may be exceedingly high for many start-ups and the customer's ever growing demand for high quality content for the lowest possible price put many teams on the spot. To solve this demand from both the market and competitors, it may be of interest to invest in the full or partial automation of content generation. By using Artificial Intelligence it is possible to solve some of the demands, saving developing time, increasing replay value and bringing players new, interesting and tailor made content. With this in mind, a study was done to expose these ideas to both the academic and gaming communities. In order to show how can such an algorithm may be put on a game, an Evolutionary Algorithm designed to make spatial content was implemented in the game Infinite Mario Bros, generating levels for it with a measure of control

Procedural Content Generation: Goals, Challenges and Actionable Steps

This chapter discusses the challenges and opportunities of procedural content generation (PCG) in games. It starts with defining three grand goals of PCG, namely multi-level multicontent PCG, PCG-based game design and generating complete games. The way these goals are defined, they are not feasible with current technology. Therefore we identify nine challenges for PCG research. Work towards meeting these challenges is likely to take us closer to realising the three grand goals. In order to help researchers get started, we also identify five actionable steps, which PCG researchers could get started working on immediately

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/

Time and Space in Video Games: A Cognitive-Formalist Approach

Video games are temporal artifacts: They change with time as players interact with them in accordance with rules. In this study, the author investigates the formal aspects of video games that determine how these changes are produced and sequenced. Theories of time perception drawn from the cognitive sciences lay the groundwork for an in-depth analysis of these features, making for a comprehensive account of time in this novel medium. This book-length study dedicated to time perception and video games is an indispensable resource for game scholars and game developers alike. Its reader-friendly style makes it readily accessible to the interested layperson

Ajuste dinâmico de dificuldade híbrido em um jogo do gênero plataforma

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2019.Conforme a indústria de videogames cresce, novos cenários surgem e os jogos devem se manter divertidos para distintos perfis de consumidor, com variados níveis de habilidades e preferências. Assim, surge um campo de pesquisa a partir da percepção e de mecanismos de ajuste da dificuldade nos jogos eletrônicos. Ou seja, um jogo pode ser tedioso quando muito fácil ou frustrante quando muito difícil, precisando oferecer um desafio contínuo e condizente ao jogador para mantê-lo motivado. A implementação de um sistema de dificuldade, ao se adaptar automaticamente à performance do jogador, pode melhorar a experiência geral do jogador com o jogo. Esses sistemas são comumente lineares, seguindo um padrão médio do público almejado. No entanto, a dificuldade pode ser adaptada de acordo com o desempenho do jogador, com seu estado afetivo ou a conjunção de ambos os modelos. No âmbito deste trabalho, objetiva-se investigar um método de estimação da dificuldade de níveis de jogos do gênero plataforma e se um mecanismo híbrido do Ajuste Dinâmico de Dificuldade (ADD) possibilita adequar a dificuldade ao jogador e mantê-lo em estado de fluxo, além de comparar sua eficiência com os outros modelos. Para isso, um jogo foi desenvolvido para se adaptar com base aos dados extraídos por algoritmos de análise de desempenho do jogador correlacionados aos obtidos por um sensor de captura de dados fisiológicos, mais especificamente pela Atividade Eletrodérmica (EDA). Além de jogar com os distintos modelos de ADD, cada participante respondeu questionários e teve seus dados coletados para averiguação dos objetos de pesquisa. O modelo híbrido demonstrou-se capaz de manter o jogador em estado de fluxo e adequar a dificuldade ao jogador, com resultados superiores aos demais modelos.As the video game industry grows, new scenarios arise and games should be entertaining for different consumer profiles with varying skill levels and preferences. Thus, a field of research emerges from the perception and mechanisms of adjustment of the difficulty in electronic games. That is, a game can be tedious when very easy or frustrating when very difficult, needing to offer a continuous and appropriate challenge to the player to keep him motivated. The implementation of a difficulty system, when adapting automatically to the performance of the player, can improve the overall experience of the player in the game. These systems are commonly linear, following the average pattern of the target audience. However, the difficulty can be adapted according to the player’s performance, his affective state or the conjunction of both models. In the scope of this work, the objective is to investigate a method that estimates the difficulty of game levels of the platform genre and if a hybrid Dynamic Difficulty Adjustment (DDA) mechanism makes it possible to adapt the difficulty to the player and keep him in a state of flow, besides comparing its efficiency with the other models. For this, a game was developed to adapt based on the data extracted by analysis algorithms of the player’s performance correlated to those obtained by a sensor that captures physiological data, more specifically by the Electrodermal Activity (EDA). In addition to playing with the different DDA models, each participant answered questionnaires and had their data collected for inquiry purposes. The hybrid model was able to keep the player in a state of flow and to adapt the difficulty to the player, with superior results compared to other models