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Users’ Evaluation of Procedurally Generated Game Levels
Abstract
Iterative design is an expensive yet necessary task in the creation of coherent game levels. However, it often requires many resources, something that many projects, especially in the academic field, are usually lacking. This paper discusses the results of a test performed on EscapeTower, a pre-existing customer-ready research game where hand-crafted levels have been replaced by procedural ones to speed up the development process. A custom room generator has been developed and used to procedurally generate several levels for the EscapeTower project. A User Study was subsequently conducted to assess how the procedurally generated levels affect the user experience within the game and how they compared to the original levels designed by professionals. Results are in line with current literature, showing that players have a significant preference over manually designed spaces. However, data also shows that procedurally generated environments did not impact users’ ability to navigate the spaces, leading to the possibility to use such systems in early prototyping and designing phases.
Designing games is a complicated and time-consuming process, where developing new levels for existing games can take weeks. Procedural content generation offers the potential to shorten this timeframe, however automated design tools are not adopted widely in the game industry. This paper presents an expert evaluation of a human-in-the-loop generative design approach for commercial game maps that incorporates multiple computational agents. The evaluation aims to gauge the extent to which such an approach could support and be accepted by human game designers, and to determine whether the computational agents improve the overall design. To evaluate the approach, eleven game designers utilized the approach to design game levels with the computational agents both active and inactive. Eye tracking, observational and think aloud data was collected to determine whether designers favored levels suggested by the computational agents. This data was triangulated with qualitative data from semi-structured interviews that were used to gather overall opinions of the approach. The eye tracking data indicates that the participating game level designers showed a clear preference for levels suggested by the computational agents, however expert designers in particular appeared to reject the idea that the computational agents are helpful. The perception of computational tools not being useful needs to be addressed if procedural content generation approaches are to fulfill their potential for the game industry.
Curiosity is a fundamental trait of human nature, and as such, it has been studied and exploited in many aspects of game design. However, curiosity is not a static trigger that can just be activated, and game design needs to be carefully paired with the current state of the game flow to produce significant reactions. In this paper we present the preliminary results of an experiment aimed at understanding how different factors such as perceived narrative, unknown game mechanics, and non-standard controller mapping could influence the evolution of players’ behaviour throughout a game session. Data was gathered remotely through a puzzle game we developed and released for free on the internet, and no description on potential narrative was provided before gameplay. Players who downloaded the game did it on their free will and played the same way they would with any other game. Results show that initial curiosity towards both a static and dynamic environment is slowly overcome by the sense of challenge, and that interactions that were initially performed with focus lose accuracy as result of players’ attention shift towards the core game mechanics.
With hundreds of new games being released every week, designers rely on existing knowledge to design control schemes for their products. However, in the case of games with new game mechanics, designers struggle to implement new button schemes due to the lack of research on players’ adaptation to new and non-standard controls. In this study we investigated PC players habits when playing a game they have no knowledge of, and how they adapt to its non-standard control scheme. Data was collected by using a specifically designed game instead of relying on pre-existing ones, allowing us to design specific game mechanics to exploit users’ habits and monitor players’ behaviour in their home environments. Preliminary results seem to indicate that PC players do pay attention to control schemes and are able to quickly learn new ones, but they also prefer to make mistakes in favour of execution speed.
This book offers a compendium of best practices in game dynamics. It covers a wide range of dynamic game elements ranging from player behavior over artificial intelligence to procedural content generation. Such dynamics make virtual worlds more lively and realistic and they also create the potential for moments of amazement and surprise.
In many cases, game dynamics are driven by a combination of random seeds, player records and procedural algorithms. Games can even incorporate the player’s real-world behavior to create dynamic responses. The best practices illustrate how dynamic elements improve the user experience and increase the replay value.
The book draws upon interdisciplinary approaches; researchers and practitioners from Game Studies, Computer Science, Human-Computer Interaction, Psychology and other disciplines will find this book to be an exceptional resource of both creative inspiration and hands-on process knowledge.
Games that use procedural content generation (PCG) do so in a wide variety of ways and for different reasons. One of the most common reasons cited by PCG system creators and game designers is improving replayability by providing a means for automatically creating near-infinite amounts of content, the player can come back and replay the game and refine her strategies over a long period. However, this notion of replayability is both overly broad and incomplete as a motivation. This paper contributes an analytical framework and associated common vocabulary for understanding the role of PCG in games from a design standpoint, with an aim of unpacking some of the broad justifications for PCG use in games, and bringing together technical concerns in designing PCG systems with design concerns related to creating engaging playable experiences.
Hundreds of millions of people play computer games every day. For them, game content—from 3D objects to abstract puzzles—plays a major entertainment role. Manual labor has so far ensured that the quality and quantity of game content matched the demands of the playing community, but is facing new scalability challenges due to the exponential growth over the last decade of both the gamer population and the production costs. Procedural Content Generation for Games (PCG-G) may address these challenges by automating, or aiding in, game content generation. PCG-G is difficult, since the generator has to create the content, satisfy constraints imposed by the artist, and return interesting instances for gamers. Despite a large body of research focusing on PCG-G, particularly over the past decade, ours is the first comprehensive survey of the field of PCG-G. We first introduce a comprehensive, six-layered taxonomy of game content: bits, space, systems, scenarios, design, and derived. Second, we survey the methods used across the whole field of PCG-G from a large research body. Third, we map PCG-G methods to game content layers; it turns out that many of the methods used to generate game content from one layer can be used to generate content from another. We also survey the use of methods in practice, that is, in commercial or prototype games. Fourth and last, we discuss several directions for future research in PCG-G, which we believe deserve close attention in the near future.
Procedural content generation (PCG) is concerned with automatically generating game content, such as levels, rules, textures and items. But could the content generator itself be seen as content, and thus generated automatically? This would be very useful if one wanted to avoid writing a content generator for a new game, or if one wanted to create a content generator that generates an arbitrary amount of content with a particular style or theme. In this paper, we present a procedural procedural level generator generator for Super Mario Bros. It is an interactive evolutionary algorithm that evolves agent-based level generators. The human user makes the aesthetic judgment on what generators to prefer, based on several views of the generated levels including a possibility to play them, and a simulation-based estimate of the playability of the levels. We investigate the characteristics of the generated levels, and to what extent there is similarity or dissimilarity between levels and between generators.
Procedural content generation (PCG) is an increasingly important area of technology within modern human-computer interaction (HCI) design. Personalization of user experience via affective and cognitive modeling, coupled with real-time adjustment of the content according to user needs and preferences are important steps toward effective and meaningful PCG. Games, Web 2.0, interface, and software design are among the most popular applications of automated content generation. The paper provides a taxonomy of PCG algorithms and introduces a framework for PCG driven by computational models of user experience. This approach, which we call Experience-Driven Procedural Content Generation (EDPCG), is generic and applicable to various subareas of HCI. We employ games as an example indicative of rich HCI and complex affect elicitation, and demonstrate the approach's effectiveness via dissimilar successful studies.
This paper provides a review of existing approaches to using evolutionary algorithms (EA) during procedural terrain generation (PTG) processes in video games. A reliable PTG algorithm would allow game maps to be created partially or completely autonomously, reducing the development cost of a game and providing players with more content. Specifically, the use of EA raises possibilities of more control over the terrain generation process, as well as the ability to tailor maps for individual users. In this paper we outline the prominent algorithms that use EA in terrain generation, describing their individual advantages and disadvantages. This is followed by a comparison of the core features of these approaches and an analysis of their appropriateness for generating game terrain. This survey concludes with open challenges for future research.
The focus of this survey is on research in applying evolutionary and other metaheuristic search algorithms to automatically generating content for games, both digital and nondigital (such as board games). The term search-based procedural content generation is proposed as the name for this emerging field, which at present is growing quickly. A taxonomy for procedural content generation is devised, centering on what kind of content is generated, how the content is represented and how the quality/fitness of the content is evaluated; search-based procedural content generation in particular is situated within this taxonomy. This article also contains a survey of all published papers known to the authors in which game content is generated through search or optimisation, and ends with an overview of important open research problems.
A correctly designed dynamic programming algorithm can be used as a fitness function to permit the evolution of maze-like levels for use in games. This study compares multiple representations for evolvable mazes including direct, as well as positive and negative indirect representations. The first direct representation simply specifies, with a binary gene, which squares of a grid are obstructed. The second paints the maze grid and passage is allowed only between colors that are the same or adjacent in a rainbow. The positive and negative representations are developmental and evolve directions for adding barriers or digging “tunnels.” These representations are tested with a design space of fitness functions that automatically generate levels with controllable properties. Fitness function design is the most difficult part of automatic level generation and this study gives a simple framework for designing fitness functions that permits substantial control over the character of the mazes that evolve. This technique relies on using checkpoints within the maze to characterize the connectivity and path lengths within the level. Called checkpoint-based fitness, these fitness functions are built on a menu of properties that can be rewarded. The choice of which qualities are rewarded, in turn, specifies within broad limits the characteristics of the mazes to be evolved. Three of the representations are found to benefit from a technique called sparse initialization in which a maze starts mostly empty and variation operators fill in details while increasing fitness. Different representations are found to produce mazes with very different appearances, even when the same fitness function is used. The example fitness functions designed around dynamic programming with checkpoints are found to permit substantial control over the properties of the evolved mazes.
This article proposes that psychological research published in APA journals focuses too narrowly on Americans, who comprise less than 5% of the world's population. The result is an understanding of psychology that is incomplete and does not adequately represent humanity. First, an analysis of articles published in six premier APA journals is presented, showing that the contributors, samples, and editorial leadership of the journals are predominantly American. Then, a demographic profile of the human population is presented to show that the majority of the world's population lives in conditions vastly different from the conditions of Americans, underlining doubts of how well American psychological research can be said to represent humanity. The reasons for the narrowness of American psychological research are examined, with a focus on a philosophy of science that emphasizes fundamental processes and ignores or strips away cultural context. Finally, several suggestions for broadening the scope of American psychology are offered.
This book presents the most up-to-date coverage of procedural content generation (PCG) for games, specifically the procedural generation of levels, landscapes, items, rules, quests, or other types of content. Each chapter explains an algorithm type or domain, including fractal methods, grammar-based methods, search-based and evolutionary methods, constraint-based methods, and narrative, terrain, and dungeon generation.
The authors are active academic researchers and game developers, and the book is appropriate for undergraduate and graduate students of courses on games and creativity; game developers who want to learn new methods for content generation; and researchers in related areas of artificial intelligence and computational intelligence.
Grammars are fundamental structures in computer science that also have many applications in procedural content generation. This chapter starts by describing a classic type of grammar, the L-system, and its application to generating plants of various types. It then describes how rules and axioms for L-systems can be created through search-based methods. But grammars are not only useful for plants. Two longer examples discuss the generation of action-adventure levels through graph grammars, and the generation of Super Mario Bros. levels through grammatical evolution.