A Progressive Approach to Content Generation

Abstract. PCG approaches are commonly categorised as constructive, generate-and-test or search-based. Each of these approaches has its distinctive advantages and drawbacks. In this paper, we propose an approach to Content Generation (CG) – in particular level generation – that combines the advantages of construc-tive and search-based approaches thus providing a fast, flexible and reliable way of generating diverse content of high quality. In our framework, CG is seen from a new perspective which differentiates between two main aspects of the game-play experience, namely the order of the in-game interactions and the associated level design. The framework first generates timelines following the search-based paradigm. Timelines are game-independent and they reflect the rhythmic feel of the levels. A progressive, constructive-based approach is then implemented to evaluate timelines by mapping them into level designs. The framework is applied for the generation of puzzles for the Cut the Rope game and the results in terms of performance, expressivity and controllability are characterised and discussed.

Progressive Content Generation Based on Cyclic Graph for Generate Dungeon

Dungeon is level in game consisting collection of rooms and doors with obstacles inside. To make good level, takes a lot of time. With Procedural Content Generation (PCG), dungeons can be created automatically. One of the approaches in PCG to create levels is progressive. Progressive approach produces timeline as representation of the interactions in the game. Timeline representation that is in the form of one straight line is good for endless runner, but for dungeon, the levels are linear. In this research, the timeline is changed to cyclic graph. Cyclic graph is formed using graph grammar algorithm. This research aims to build dungeon that has not linear and minimal dead ends. To eliminate linearity in dungeons, branching in dungeons needs to be formed. The steps carried out in this research are designing graph grammar rules, generating population of graphs, evaluating graphs with fitness values, and building dungeons. Four functions are used to determine the fitness value: shortest vertices, average duration, replayability, and variation. Dungeons produced with progressive approach manage to minimize linearity in dungeons. Dungeon formation is very dependent on the rule grammar that forms it. With the evaluation process, linear dungeons resulting from grammar rules can be minimized

Automatic puzzle level generation : a general approach using a description language

In this paper, we present a general technique to generate and evaluate puzzle levels made by Puzzle Script. Puzzle Script is a videogame description language created by Stephen Lavelle for scripting puzzle games. We propose a system to help in generating levels for Puzzle Script without any restriction on the current game rules. Two different approaches are used with a trade off between speed (Constructive approach) and playability (Genetic approach). These two approaches use a level evaluator that calculates the scores of the generated levels based on their playability and challenge. The generated levels are assessed by human players statistically, and the results show that the constructive approach is capable of generating playable levels up to 90%, while genetic approach can reach up to 100%. The results also show a high correlation between the system scores and the human scores.peer-reviewe

Literature review of procedural content generation in puzzle games

This is the third chapter from my Master Thesis (Automatic Game Generation). This chapter will provide a review of the past work on Procedural Content Generation. It highlights different efforts towards generating levels and rules for games. These efforts are grouped according to their similarity and sorted chronologically within each group.N/

Generation and Analysis of Content for Physics-Based Video Games

The development of artificial intelligence (AI) techniques that can assist with the creation and analysis of digital content is a broad and challenging task for researchers. This topic has been most prevalent in the field of game AI research, where games are used as a testbed for solving more complex real-world problems. One of the major issues with prior AI-assisted content creation methods for games has been a lack of direct comparability to real-world environments, particularly those with realistic physical properties to consider. Creating content for such environments typically requires physics-based reasoning, which imposes many additional complications and restrictions that must be considered. Addressing and developing methods that can deal with these physical constraints, even if they are only within simulated game environments, is an important and challenging task for AI techniques that intend to be used in real-world situations. The research presented in this thesis describes several approaches to creating and analysing levels for the physics-based puzzle game Angry Birds, which features a realistic 2D environment. This research was multidisciplinary in nature and covers a wide variety of different AI fields, leading to this thesis being presented as a compilation of published work. The central part of this thesis consists of procedurally generating levels for physics-based games similar to those in Angry Birds. This predominantly involves creating and placing stable structures made up of many smaller blocks, as well as other level elements. Multiple approaches are presented, including both fully autonomous and human-AI collaborative methodologies. In addition, several analyses of Angry Birds levels were carried out using current state-of-the-art agents. A hyper-agent was developed that uses machine learning to estimate the performance of each agent in a portfolio for an unknown level, allowing it to select the one most likely to succeed. Agent performance on levels that contain deceptive or creative properties was also investigated, allowing determination of the current strengths and weaknesses of different AI techniques. The observed variability in performance across levels for different AI techniques led to the development of an adaptive level generation system, allowing for the dynamic creation of increasingly challenging levels over time based on agent performance analysis. An additional study also investigated the theoretical complexity of Angry Birds levels from a computational perspective. While this research is predominately applied to video games with physics-based simulated environments, the challenges and problems solved by the proposed methods also have significant real-world potential and applications

Avaliação de redes neurais para a geração de imagens

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Sistemas de Informação.Estúdios e equipes de desenvolvimento independentes tem ficado cada vez mais comuns nos últimos anos, com muitos destes se deparando com bastante sucesso. Em função do aumento da acessibilidade a computadores, dispositivos e à Internet, a prospecção de ser um desenvolvedor independente tem se mostrado uma idéia cada vez mais atrativa. Muitas vezes, essas equipes ou estúdios são compostos por apenas uma pessoa - esta sendo, geralmente, um(a) programador(a). Com restrições de recursos, nem sempre se pode pagar por profissionais de outras áreas, tais como arte e música, para auxiliar na criação de jogos. Soluções comuns para isso são motores de jogos (do inglês, game engines), que facilitam a criação de componentes de jogos. Mesmo assim, ainda sobram muitas atividades que o desenvolvedor deve fazer manualmente, especialmente no que diz respeito à arte do jogo. Programas capazes de automatizar tais processos manuais e demorados certamente se fazem ser extremamente úteis, uma vez que reduz consideravelmente o tempo e dinheiro necessários para a execução das mesmas. O objetivo deste trabalho foi de realizar uma análise comparativa de algumas abordagens com redes neurais para a geração de imagens para que, futuramente, possa ser criada uma ferramenta baseada na abordagem mais apropriada encontrada que resolva, pelo menos em parte, o problema de reduzir o custo de criação de alguns dos componentes de arte de jogos, mais especificamente de objetos individuais (i.e.: árvores, animais, pessoas/rostos, etc). Para tal, foi realizada uma comparação entre redes recorrentes, convolucionais e redes generativas adversariais, na forma de auto-codificadores, em cima do conjunto MNIST e, no caso da convolucional, em cima do conjunto Cifar10, para testar a viabilidade de se utilizar estas redes para resolver este problema. Através da aplicação de técnicas de aprendizagem de máquina e redes neurais, ao final dos experimentos realizados com algumas arquiteturas diferentes de redes neurais, foi demonstrado que estas conseguem ser ferramentas poderosas na tarefa de geração automatizada de imagens