A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Procedural Content Generation (PCG) <s> We try to clarify the concept of procedural content generation (PCG) through contrasting it to other forms of content generation in games with which it could easily be mistaken, and through discussing some properties of PCG which are sometimes thought of as necessary but are actually not. After drawing up some clear demarcations for what is and what is not PCG, we present two versions of a content generation system for Infinite Mario Bros which is intentionally designed to question these same demarcations. We argue that, according to our own definition, one version of the system is an example of PCG while the other is not, even though they are mostly identical. We hope that this paper answers some questions but raises others, and inspires researchers and developers to thread some less common ground in developing content generation techniques. <s> BIB001 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Procedural Content Generation (PCG) <s> As virtual worlds in games become larger and more detailed, the need for rich, interactive content to realistically populate these worlds becomes greater. Content is expensive and slow to create manually and does not scale once created. Procedural content generation offers an attractive alternative for providing this content. There are several methods for generating different types of content including terrain and some types of organisms. However, these methods are hard to control and can give inconsistent results. A method of providing a high level, semantic context to such procedural methods may offer a potential solution. Such an approach may also allow manually created content to be placed appropriately and in a scalable manner in the world. Finally, semantic knowledge may be used to annotate content to allow greater interactivity and improved AI. This paper suggests such a method. Making use of semantic networks for storing knowledge about the potential content of the virtual world and using stateful graph traversal algorithms to convert the semantic knowledge into concrete instances, this method supports the procedural generation of rich complex content for virtual worlds. <s> BIB002 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Procedural Content Generation (PCG) <s> Balancing the interactions between game level design and intended player experience is a difficult and time consuming process. Automating aspects of this process with respect to user-defined constraints has beneficial implications for game designers. A change in level layout may affect the available routes and subsequent player interactions for a number of agents within the level. Small changes in the placement of game elements may lead to significant changes in terms of the challenge experienced by the player on the path to their goal. Estimating the effect of this change requires that the designer take into account new paths of all interacting agents and how these may affect the player. As the number of these agents grow to crowd size, estimating the effect of these changes becomes grows difficult. We present a user-in-the-loop framework for tackling this task by optimizing enemy agent settings and the placement of game elements that affect the flow of agents within the level, with respect to estimated difficulty. Using static path analysis we estimate difficulty based on agent interactions with the player. To exemplify the usefulness of the framework, we show that small changes in level layout lead to significant changes in game difficulty, and optimizations with respect to the characterization of difficulty can be used to attain desired difficulty levels. <s> BIB003 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Procedural Content Generation (PCG) <s> We present an exploratory analysis of the effects of game world topologies on self-reported player experience in Computer Role Playing Games (CRPGs). We find that (a) players are more engaged in game worlds that better match their self-reported preferences; and (b) player preferences for game topology can be predicted based on their in-game behavior. We further describe how in-game behavioral features that correlate to preferences can be used to control procedural content generation algorithms. <s> BIB004 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Procedural Content Generation (PCG) <s> Recent evaluations of Procedural Content Generation (PCG) methods have examined the use of personas as part of there evaluative functions. Personas, models of a user, have a number of complaints leveled against them by researchers as to their use as design tools: that there is poor empirical proof that they improve the design process, poor definitions lead to them not modeling actual uses, and they do not give any quantitative information for evaluation using a scientific approach. Examined is a framework in which a persona is not just defined as a model of a user, but is a contract list of player goals and actions available in the game. By defining a persona as a goal driven AI, it joins the idea of personas with a more qualitative evaluative method as well as not allowing developers to apply their own biases. Further, it allows for a persona to work beyond the design stage of the development, and into the maintenance stages. Taking into account game-play telemetry, it allows for personas to be used as tools for lessons learned for future developments. <s> BIB005 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Procedural Content Generation (PCG) <s> 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. <s> BIB006 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Procedural Content Generation (PCG) <s> We survey across generations of work in authorized cheat sheets, and emphasize on extracting generic features for designing one. We construct generic models to address cognitive aspects of learning and focus on the content of knowledge components in the domain of programming language learning. We evaluate the models with real classroom study data and found that the amount of notes on a cheat sheet is associated with gains in students' declarative knowledge, but not procedural knowledge. We also discovered that working in creating new cheat sheets can help students to achieve better exam grades accumulatively beyond and above the effect of their performance in a prior exam. We discuss how these findings can guide the design of technology-based study guides. <s> BIB007 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Procedural Content Generation (PCG) <s> So far, Evolutionary Algorithms (EA) have been the dominant paradigm for Procedural Content Generation (PCG). While we believe the field has achieved a remarkable success, we claim that there is a wide window for improvement. The field of machine learning has an abundance of methods that promise solutions to some aspects of PCG that are still under-researched. In this paper, we advocate the use of Intrinsically motivated reinforcement learning for content generation. A class of methods that thrive for knowledge for its own sake rather than as a step towards finding a solution. We argue that this approach promises solutions to some of the well-known problems in PCG: (1) searching for novelty and diversity can be easily incorporated as an intrinsic reward, (2) improving models of player experience and generation of adapted content can be done simultaneously through combining extrinsic and intrinsic rewards, and (3) mix-initiative design tools can incorporate more knowledge about the designer and her preferences and ultimately provide better assistance. We demonstrate our arguments and discuss the challenges that face the proposed approach. <s> BIB008
On this research, PCG is defined as "the algorithmic creation of game content with limited or indirect user input" based on BIB001 . The term "content" in PCG are defined as anything that exist and contained in a game, which includes levels, maps, game rules, etc. BIB006 , while the term game is more strictly defined and categorized narrowly into digital games. PCG work types are defined as the types of work being done by using PCG as a means of delivery. The work types are divided into 4 categories: (i) analysis, (ii) framework, (iii) game, and (iv) generator. Analysis work type focuses only on the analysis aspect of any existing PCG method or analyzing a comparison between different method of PCG. Analysis work type focuses more on planning, assessment, commentaries, or a planned model. This work type category does not include any form of developed framework or developed content or creation of a new content or new form of PCG. Framework work type extends the analysis work type with a theoretical and planned model along with an elaboration of such plan. However, this work type does not create an entire game nor shows a generated content that is measurable as there is no content being created to be measured. This work type nor does the work type also does not intend to continue its work into a fully working game, but instead only a fraction of the game or a part of an existing game. Game work type further extends the framework work type by using PCG to generate an entire game as a generated content. This work type does not include work type that generates contents of a game, but focused more into works that creates an entire game by using PCG as its main means of delivery. As such, the game generated are evaluated and considered as the final output of this kind of work. While game work type focuses on the entire object of a game and views the PCG content generated as an output, generator work type focuses more into a single aspect being generated in the game and measures the content generated instead of the entire game itself being evaluated. Based on figure 4, a vast majority of the papers analyzed focuses on generator work type which is not a surprise as generating a single aspect inside a game by using PCG is far easier compared to generating a game by using PCG as its main means of delivery, while at the same time also shows a more advanced work being done compared only to preliminary ideas. As such, implementation and development of contents as a proof-ofconcept and evaluating them in a testbed is particularly important on works that focuses on PCG, while it is not entirely necessary to do such for novel and newer preliminary ideas which require further testing. Form of analysis work type being done is more of an exploratory analysis of a particular method BIB004 , inferring external aspects based on works being done by using PCG  - BIB007 which mostly focuses on socio-cultural aspect in term of existing game usage, or creating an analysis of methodology of currently existing method of PCG  . Another form of analysis work type based on paper analyzed is on argumentation of an existing method of PCG along with its reinforced ideas. A small number of papers which focuses on framework mostly focuses on improving existing ideas with models to increase its legitimacy and novelty, such works may also count as a preliminary work as the work being done are not being tested yet but the argumentation and theory along with a model has been proposed to tackle a particular problem, usually it is with attached future works for further development phase. Such framework work type may include diagrams BIB005 , system architecture  , or intricate algorithm and basic design phase BIB002 , BIB008 . However, some frameworks might be tested as it is a generator by using fitness evaluation and analysis BIB003 , thus it is not entirely possible to categorize one work type into an independent or strict category based on its work type. Addressing the majority of paper found, generator is the most common type of PCG implementation. Based on the paper found, majority of the papers that focuses on content generation is either fully generating contents for the sake of the PCG, or using PCG as an applicative means to create content for games -be it an existing games or new game entirely.