Abstract Procedural Content Generation means the algorithmic creation of game content with limited or indirect user input. This technique is currently widespread in the game industry. However, its effects when applied to elements that do not engage directly with the player, also known as Game Bits, require more research. This paper focuses on how players experience a game when these Game Bits are procedurally generated, and how this alters their will to continue playing the game. By developing and using a 2D Roguelike game to perform a qualitative study with eight participants, this dissertation shows an indication that procedurally generating Game Bits does not alter how the players experience a game or their desire to replay it. Keywords: Procedural content generation, PCG, Game Bits, Game User Experience, Roguelike. Table of Contents 1. Introduction 1 2. Background 2 2.1 Procedural Content Generation 2 2.2 PCG Methods 3 2.3 Layers of Game Content 4 2.4 PCG in games 5 2.5 Roguelikes 6 2.6 User Experience (UX) and Game User Experience 7 2.7 Color and emotion 10 2.8 Related studies 10 3. Problem 12 3.1 Method 13 3.2 Ethical considerations 15 4. The game prototype 16 4.1 Core mechanics and User Interface 16 4.2 Environment 18 4.3 Enemies 19 4.4 Environmental objects 22 5. Results and analysis 23 5.1 Participants and their background 23 5.2 General impression of the game 24 5.3 Enjoyment 25 5.4 Frustration 27 5.5 Replayability 28 5.6 Environment and aesthetic value 30 5.7 Analysis 32 6. Conclusions 34 6.1 Summary 34 6.3 Discussion 37 6.3 Future Work 39 References 40 Appendix A I Appendix B III 1. Introduction Procedural Content Generation (PCG) is the creation of game content by algorithmic means, independently of the degree of input introduced by the designer. Its benefits and possible applications are many. PCG can decrease the amount of work needed to produce a vast number of assets, thus decreasing time and cost expenses when developing a game. In addition, it also allows developers to experiment with randomization and produce unexpected results, opening new possibilities in the game industry. Interactive narrative and dungeon generation are examples of that. This technology can be applied to several aspects of any game. To be able to analyze the possible effects PCG has on these elements, this dissertation uses the hierarchy of layers of game content proposed by Hendrikx et al (2013). Two of these categories are of most importance to this study: Game Bits and Game Space. Game Bits is the layer that contains all the elements of a game that do not have any active effect on gameplay, like textures and sounds, and Game Space represents the different virtual spaces in which any game takes place. The reason why these layers are focused on is that they are the most common target for PCG, especially in some game genres. One of those genres is Roguelikes. These types of games are characterized by their lack of progression, resetting the game completely after every run, regardless of the player either succeeding or failing. To be able to offer some replayability, these games feature implementation of PCG in their Game Space layer, meaning their levels, usually represented by dungeon floors, are completely generated from scratch by an algorithm every run. In modern adaptations of this genre, the application of PCG has also extended to the Game Bits layer, offering some variance in the background or environmental elements. Since this technology is becoming a common practice within the game industry, it is interesting to study how its implementation might affect game user experience. This dissertation develops a 2D roguelike game from scratch in order to properly test and analyze the effects PCG has on the players’ levels of enjoyment when implemented in the Game Bits layer, and how it might also affect the replayability of the game. The outcome of the experiment shows that, when focusing on the Game Bits layer, PCG has no major impact on players’ enjoyment and desire to replay a game. After analyzing the results, possible factors that might have affected the outcome of the experiment are isolated, and a set of suggestions to further improve future implementations of PCG in the Game Bits layer is suggested. 1 2. Background 2.1 Procedural Content Generation Shaker, Togelius and Nelson (2016) define Procedural Content Generation (PCG from now on) as “the algorithmic creation of game content with limited or indirect user input” (p. 1). This implies that a program run by a computer is able to generate something on its own, or with little human input. From their perspective, they limit the content that can be procedurally generated in games to the different elements that compose them; from the different levels and maps to the many textures and objects that can be found in every game. However, they decided to exclude the game engine itself and the different Artificial Intelligences that are often present in games. In a similar way, Summerville et al. (2018) consider PCG to be “the creation of game content through algorithmic means”, which leaves some space to interpret to what degree the algorithm should create the content on its own, or what kind of content it should create. Hendrikx et al. (2013) describe PCG as “the application of computers to generate game content, distinguish interesting instances among the ones generated, and select entertaining instances on behalf of the players” (p. 2). This definition, however, seems to be oriented towards specific methods of PCG that will be described in chapter 2.2: search-based and solver-based generation. Even so, this adds another challenge to PCG when using these methods: the software has to be able to not only generate content but also judge the instances created using pre-established values and choosing the most suitable ones. Without a method to supervise the possible outcome, the results of PCG can be unpredictable and not always as well perceived as authored content might be. So why risk the quality of the resulting product to have a program generate content for the designers? There are several reasons for that. For instance, the economic benefit of not requiring an artist to produce ​ every piece or component that is featured in the game. Whether the algorithm is producing most of the graphical and design content or only part of it, this can reduce costs considerably. Directly linked to this effect, PCG also helps to reduce the development ​ time. On the other side of the spectrum, PCG could also be used as a tool to increase ​ ​ artists’ creativity, allowing even smaller development studios to generate a large amount ​ of content by skipping a part of the design process. PCG is not only limited to generating graphical assets. When paired with level and environment generation it can potentially boost a game’s replayability. It also solves possible memory constraints, as the ​ ​ ​ different assets that would occupy that memory space could be created on the fly. Furthermore, PCG opens new possibilities as games could generate content during run ​ time that adapts and tailors itself around the player’s tastes and behavior, by using player modeling and neural networks (Khaled, Nelson and Barr, 2013; Togelius et al., 2013; Shaker, Togelius, and Nelson, 2016). Naturally, PCG not only brings advantages to the development process and the product. Implementing it can carry a set of challenges and disadvantages that must be considered before continuing with the process. One possible disadvantage is the evaluation of the PCG process. In order to make the generation system more accessible and malleable, additional control systems can be implemented in the tool. However, this can make the interface ​ 2 overly complicated and hard to work with, augmenting the development time and ​ undoing part of what was considered an advantage in the paragraph above. One way to counter this negative effect is by applying generate-and-test methods (Frade, de Vega, and Cotta, 2012). In a similar way, when both authored and procedurally generated content have to be implemented in the same space there are some issues that have to be solved, such as properly balancing user control and automatic model consistency maintenance and integrating both authored content and PCG in the same iterative process (Smelik et al., 2010). 2.2 PCG Methods This chapter will briefly describe the main general procedure to implement procedurally generated content in a game. Since it is not the objective of this dissertation to analyze or categorize the different methods that can be used and especially considering the vast number of options that are available, this description will be kept concise and general, and focus on the two basic ways to integrate PCG from which many other methods derive. Constructive generation methods are a family of algorithms that produce just a single instance of the chosen content each time they are executed. Due to their relative simplicity, constructive generation methods are usually very fast processes, some of them being able to produce content at runtime. On the other side of the coin, without proper fine-tuning these methods offer little control over the overall process, limiting the designer’s input and generating inconsistent content, i.e. an unplayable level (Shaker et al., 2016). Procedural generation has the risk of producing content that does not meet the expected quality or even creating broken content. Constructive generation methods produce content once and do not test the output so, in order to avoid producing defective content, these methods usually limit themselves to using only systems of operations that are guaranteed to succeed.