The Dungeon Variations Problem Using Constraint Programming

The video games industry generates billions of dollars in sales every year. Video games can offer increasingly complex gaming experiences, with gigantic (but consistent) open worlds, thanks to larger and larger teams of developers and artists. In this paper, we propose a constraint-based approach for procedural dungeon generation in an open world/universe context, in order to provide players with consistent, open worlds with an excellent quality of storytelling. Thanks to a global description capturing all the possible rooms and situations of a given dungeon, our approach allows enumerating variations of this global pattern, which can then be presented to the player for more diversity. We formalise this problem in constraint programming by exploiting a graph abstraction of the dungeon pattern structure. Every path of the graph represents a possible variation matching a given set of constraints. We introduce a new propagator extending the "connected" graph constraint, which allows considering directed graphs with cycles. We show that thanks to this model and the proposed new propagator, it is possible to handle scenarios at the forefront of the game industry (AAA+ games). We demonstrate that our approach outperforms non-specialised solutions consisting of filtering only the relevant solutions a posteriori. We then conclude and offer several exciting perspectives raised by this approach to the Dungeon Variations Problem

Measuring quality of grammars for procedural level generation

Grammar-based procedural level generation raises the productivity of level designers for games such as dungeon crawl and platform games. However, the improved productivity comes at cost of level quality assurance. Authoring, improving and maintaining grammars is difficult because it is hard to predict how each grammar rule impacts the overall level quality, and tool support is lacking. We propose a novel metric called Metric of Added Detail (MAD) that indicates if a rule adds or removes detail with respect to its phase in the transformation pipeline, and Specification Analysis Reporting (SAnR) for expressing level properties and analyzing how qualities evolve in level generation histories. We demonstrate MAD and SAnR using a prototype of a level generator called Ludoscope Lite. Our preliminary results show that problematic rules tend to break SAnR properties and that MAD intuitively raises flags. MAD and SAnR augment existing approaches, and can ultimately help designers make better levels and level generators

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

Scare Tactics

It is the purpose of this document to describe the design and development processes of Scare Tactics. The game will be discussed in further detail as it relates to several areas, such as market analysis, development process, game design, technical design, and each team members’ individual area of background research. The research areas include asymmetrical game design, level design, game engine architecture, real-time graphics, user interface design, networking and artificial intelligence. As part of the team’s market analysis, other games featuring asymmetric gameplay are discussed. The games described in this section serve as inspirations for asymmetric game design. Some of these games implement mechanics that the team seeks to emulate and expand upon in Scare Tactics. As part of the team’s development process, several concepts were prototyped over the course of two months. During that process the team adopted an Agile methodology in order to assist with scheduling, communication and resource management. Eventually, the team chose to expand upon the prototype that became the basis of Scare Tactics. Game design and technical design occur concurrently in the development of Scare Tactics. Designers conduct discussions where themes, settings, and mechanics are conceived and documented. Mechanics are prototyped in Unity and eventually ported to a proprietary engine developed by our team. Throughout the course of development, each team member has had to own an area of design or development. This has led to individual research performed in several areas, which will be discussed further in this document

A general-purpose expressive algorithm for room-based environments

This paper presents a generative architecture for general-purpose room layouts that can be treated as geometric definitions of dungeons, mansions, shooter levels and more. The motivation behind this work is to provide a design tool for virtual environments that combines aspects of controllability, expressivity and generality. Towards that end, a two-tier level representation is realized, with a graph-based design specification constraining and guiding the generated geometries, facilitated by constrained evolutionary search. Expressivity is secured through quality-diversity search which can provide the designer with a broad variety of level layouts to choose from. Finally, the generator is general-purpose as it can produce layouts based on different types of static grid structures or as freeform, curved structures through an adaptive Voronoi diagram that is evolved along with the level itself. The method is tested on a variety of design specifications and grid types, and results show that even with complex design constraints or malleable grids the algorithm can produce a broad variety of levels.peer-reviewe

Increasing generality in machine learning through procedural content generation

Procedural Content Generation (PCG) refers to the practice, in videogames and other games, of generating content such as levels, quests, or characters algorithmically. Motivated by the need to make games replayable, as well as to reduce authoring burden, limit storage space requirements, and enable particular aesthetics, a large number of PCG methods have been devised by game developers. Additionally, researchers have explored adapting methods from machine learning, optimization, and constraint solving to PCG problems. Games have been widely used in AI research since the inception of the field, and in recent years have been used to develop and benchmark new machine learning algorithms. Through this practice, it has become more apparent that these algorithms are susceptible to overfitting. Often, an algorithm will not learn a general policy, but instead a policy that will only work for a particular version of a particular task with particular initial parameters. In response, researchers have begun exploring randomization of problem parameters to counteract such overfitting and to allow trained policies to more easily transfer from one environment to another, such as from a simulated robot to a robot in the real world. Here we review the large amount of existing work on PCG, which we believe has an important role to play in increasing the generality of machine learning methods. The main goal here is to present RL/AI with new tools from the PCG toolbox, and its secondary goal is to explain to game developers and researchers a way in which their work is relevant to AI research

10 years of the PCG workshop : past and future trends

As of 2020, the international workshop on Procedural Content Generation enters its second decade. The annual workshop, hosted by the international conference on the Foundations of Digital Games, has collected a corpus of 95 papers published in its first 10 years. This paper provides an overview of the workshop’s activities and surveys the prevalent research topics emerging over the years.peer-reviewe

Node-Based Native Solution to Procedural Game Level Generation

A Geração Procedural de Conteúdo (PCG) aplicada ao domínio do desenvolvimento de jogos tem se tornado um tópico proeminente, com um número crescente de implementações e aplicações. Soluções de PCG standalone e plugin, regidas por interfaces baseadas em nós e outros modelos de alto nível, enfrentam limitações em termos de integração, interatividade e responsividade quando inseridas no processo de desenvolvimento de jogos. Essas limitações afetam a experiência do utilizador e inibem o verdadeiro potencial que estes sistemas podem oferecer. Adotando uma metodologia de Action-Research, realizou-se um estudo preliminar com entrevistas a especialistas da área. A avaliação da pertinência desta metodologia nativa e da abordagem visual mais adequada para a sua interface foi efetuada através de uma série de protótipos. Posteriormente, foi implementado um protótipo funcional e conduzido um estudo de caso com uma amostra constituída por um grupo de especialistas em PCG e de desenvolvedores de jogos. Os participantes realizaram uma série de exercícios que estavam documentados com os respetivos tutoriais. Após a conclusão dos exercícios propostos, os participantes avaliaram a relevância da solução e da experiência do utilizador através de um questionário. No desenvolvimento de uma metodologia nativa de PCG baseado em nós, integrado no motor de jogo, identificamos limitações e concluímos que existem diversos desafios ainda por superar no que diz respeito a uma implementação completa de um sistema complexo e amplo.Procedural Content Generation (PCG) applied to game development has become a prominent topic with increasing implementations and use cases. However, existing standalone and plugin PCG solutions, which use Node-based interfaces and other high-level approaches, face limitations in integration, interactivity, and responsiveness within the game development pipeline. These limitations hinder the overall user experience and restrain the true potential of PCG systems. Adopting an Action-Research methodology, a preliminary interview was conducted with experts in the field. The relevance assessment of this native methodology and the most suitable visual approach for its interface was carried out through a series of prototypes. Subsequently, a functional prototype was implemented, and a case study was conducted using a sample consisting of a group of PCG experts and game developers. The participants performed a series of exercises documented with the respective tutorials. After completing the exercises, the solution's relevancy and user experience were evaluated through a questionnaire. In developing a native node-based PCG methodology integrated into the game engine, we identified limitations. We concluded that several challenges are yet to be overcome regarding fully implementing a complex and extensive system