A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Introduction <s> In this paper, we show that personalized levels can be automatically generated for platform games. We build on previous work, where models were derived that predicted player experience based on features of level design and on playing styles. These models are constructed using preference learning, based on questionnaires administered to players after playing different levels. The contributions of the current paper are (1) more accurate models based on a much larger data set; (2) a mechanism for adapting level design parameters to given players and playing style; (3) evaluation of this adaptation mechanism using both algorithmic and human players. The results indicate that the adaptation mechanism effectively optimizes level design parameters for particular players. <s> BIB001 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Introduction <s> Procedural content generation is an increasingly important area of technology within modern human-computer interaction with direct applications in digital games, the semantic web, and interface, media and software design. The personalization of experience via the modeling of the user, coupled with the appropriate adjustment of the content according to user needs and preferences are important steps towards effective and meaningful content generation. This paper introduces a framework for procedural content generation driven by computational models of user experience we name Experience-Driven Procedural Content Generation. While the framework is generic and applicable to various subareas of human computer interaction, we employ games as an indicative example of content-intensive software that enables rich forms of interaction. <s> BIB002 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Introduction <s> 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. <s> BIB003 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Introduction <s> The development of a complex game is a time consuming task that requires a significant amount of content generation, including terrains, objects, characters, etc that requires a lot of effort from the a designing team. The quality of such content impacts the project costs and budget. One of the biggest challenges concerning the content is how to improve its details and at the same time lower the creation costs. In this context procedural content generation techniques can help to reduce the costs associated with content creation. This paper presents a survey of classical and modern techniques focused on procedural content generation suitable for game development. They can be used to produce terrains, coastlines, rivers, roads and cities. All techniques are classified as assisted (require human intervention/guidance in order to produce results) or non-assisted (require few or no human intervention/guidance to produce the desired results). <s> BIB004 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Introduction <s> This paper examines the literature on computer games and serious games in regard to the potential positive impacts of gaming on users aged 14 years or above, especially with respect to learning, skill enhancement and engagement. Search terms identified 129 papers reporting empirical evidence about the impacts and outcomes of computer games and serious games with respect to learning and engagement and a multidimensional approach to categorizing games was developed. The findings revealed that playing computer games is linked to a range of perceptual, cognitive, behavioural, affective and motivational impacts and outcomes. The most frequently occurring outcomes and impacts were knowledge acquisition/content understanding and affective and motivational outcomes. The range of indicators and measures used in the included papers are discussed, together with methodological limitations and recommendations for further work in this area. <s> BIB005 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Introduction <s> Adapting game content to a particular player's needs and expertise constitutes an important aspect in game design. Most research in this direction has focused on adapting game difficulty to keep the player engaged in the game. Dynamic difficulty adjustment, however, focuses on one aspect of the gameplay experience by adjusting the content to increase or decrease perceived challenge. In this paper, we introduce a method for automatic level generation for the platform game Super Mario Bros using grammatical evolution. The grammatical evolution-based level generator is used to generate player-adapted content by employing an adaptation mechanism as a fitness function in grammatical evolution to optimize the player experience of three emotional states: engagement, frustration and challenge. The fitness functions used are models of player experience constructed in our previous work from crowd-sourced gameplay data collected from over 1500 game sessions. <s> BIB006 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Introduction <s> 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. <s> BIB007 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Introduction <s> The frequent repetition of visual assets, such as the frequent appearance of a particular game object in a game world or the repetition of a character's animation cycle, often becomes apparent to players when they encounter such repetition within a short period of time. A certain amount of visual repetition has always been accepted by players, however as technology improves and game worlds tend towards more detail, the repetition of assets in games becomes more obvious. Particularly graphically advanced games require an increasing number of assets to hide the repetition and to create believable game worlds. This paper examines various levels of asset repetition in electronic games and addresses problems that can arise. The paper describes some contemporary approaches used by artists in the industry to hide repetition, and touches on current technologies that might be applied in game development to address this problem. <s> BIB008 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Introduction <s> Abstract This paper presents a procedural content generation (PCG) method that is able to generate aesthetic maps for a real-time strategy game. The maps were characterized based on either their geometrical properties or their topological measures (obtained in this latter case from the sphere-of-influence graph induced by each map). Using these features, a distance function between maps can be defined. This function is used in turn to determine how close/far each map generated by the PCG method (a self-adaptive evolutionary algorithm) is to a collection of maps which were taken initially to be aesthetic or non-aesthetic. This correspondence guided a multi-objective evolutionary approach whereby maps close to aesthetic maps and far to non-aesthetic maps are sought. Self-organizing maps are used to ascertain whether the so-generated maps naturally cluster together with aesthetic maps, as well as to provide a qualitative assessment of the ability of each set of features to characterize the latter. <s> BIB009 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Introduction <s> OBJECTIVE ::: Serious games may be effective in promoting sexual health behavior. Their confidential nature may encourage users to discuss sensitive sexuality topics. Furthermore, they can tailor messages to the individual's needs and may be intrinsically motivating. This meta-analysis investigates the effectiveness of interventions for sexual health promotion that use serious games. ::: ::: ::: MATERIALS AND METHODS ::: A database search was conducted in PubMed, Web of Science, CINAHL, and PsycINFO for publications before the end of July 2013. Serious digital games studies measuring effects on behavior or its determinants, using a control condition, allowing the calculation of an effect size (Hedges' g, random-effects model) were included. ::: ::: ::: RESULTS ::: Seven game studies for sexual health promotion were included. These showed positive effects on determinants (g=0.242; 95 percent confidence interval, 0.129, 0.356), albeit of small effect size. The effects on behavior, measured in only two studies, were not significant (g=0.456; 95 percent confidence interval, -0.649, 1.561). Most games did not use many game features that are considered to be immersive or enhancing flow. Instead, there was a strong reliance on pure gamification features, such as rewards and feedback. ::: ::: ::: CONCLUSIONS ::: The effectiveness of the next generation of games may be enhanced by building on the behavioral change and educational gaming literatures (e.g., using role-play and simulation game formats, individual tailoring, offering adaptation in the difficulty of the challenge, and amount and timing of the feedback). There is a need for studies with rigorous evaluations of game effectiveness, longer-term follow-up, and using measures of behavior rather than merely their determinants. <s> BIB010 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Introduction <s> In this meta-analysis, we systematically reviewed research on digital games and learning for K–16 students. We synthesized comparisons of game versus nongame conditions (i.e., media comparisons) and comparisons of augmented games versus standard game designs (i.e., value-added comparisons). We used random-effects meta-regression models with robust variance estimates to summarize overall effects and explore potential moderator effects. Results from media comparisons indicated that digital games significantly enhanced student learning relative to nongame conditions (g¯ = 0.33, 95% confidence interval [0.19, 0.48], k = 57, n = 209). Results from value-added comparisons indicated significant learning benefits associated with augmented game designs (g¯ = 0.34, 95% confidence interval [0.17, 0.51], k = 20, n = 40). Moderator analyses demonstrated that effects varied across various game mechanics characteristics, visual and narrative characteristics, and research quality characteristics. Taken together, the resu... <s> BIB011 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Introduction <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> BIB012 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Introduction <s> A particular challenge of the game design process is when the designer is requested to orchestrate dissimilar elements of games such as visuals, audio, narrative and rules to achieve a specific play experience. Within the domain of adversarial first person shooter games, for instance, a designer must be able to comprehend the differences between the weapons available in the game, and appropriately craft a game level to take advantage of strengths and weaknesses of those weapons. As an initial study towards computationally orchestrating dissimilar content generators in games, this paper presents a computational model which can classify a matchup of a team-based shooter game as balanced or as favoring one or the other team. The computational model uses convolutional neural networks to learn how game balance is affected by the level, represented as an image, and each team's weapon parameters. The model was trained on a corpus of over 50,000 simulated games with artificial agents on a diverse set of levels created by 39 different generators. The results show that the fusion of levels, when processed by a convolutional neural network, and weapon parameters yields an accuracy far above the baseline but also improves accuracy compared to artificial neural networks or models which use partial information, such as only the weapon or only the level as input. <s> BIB013 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Introduction <s> This paper presents a coevolutionary framework for procedural generation of NPCs in MMORTS games. In this context, players need to defeat environmental troops in battle to gather resources and achieve their goals. The benchmarked game has several balance problems related to these battle activities, mostly caused by the handcraft design of complex game content. To solve this problem, the algorithm takes player modeled heroes as input and returns a solution evolved to win. By this way, the players need to think better in a new way to conquer the victory, adding new levels of challenge, keeping the game enjoyable. We present a new mathematical model to evaluate the solutions, based only on the number of soldiers on the input and output, making it easy to extend to other contexts. The results show it is possible to procedurally generate thousands of new efficient and fair builds, without violating the game rules. Moreover, our analysis of the results was able to identify unbalanced characteristics in the game design and we suggested simple way to fix it. <s> BIB014 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Introduction <s> Although there has been much work on procedural content generation for other game genres, very few researchers have tackled automated content generation for educational games. In this paper, we present a template-based, automatic puzzle generator for an educational puzzle programming game called BOTS. Two experts created their own new puzzles and evaluated generator-generated puzzles for meeting the educational goals, the structural and visual novelty. We show that our generator can generate puzzles with expert-designed educational goals while saving experts more than 80% of creation time, and these puzzles exhibit structural and visual novelty compared to expert-created puzzles. The contribution of this work is defined and implemented the first template-based automatic puzzle generator that saves expert time while incorporating expert-designed educational goals and enhancing puzzle creativity. <s> BIB015 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Introduction <s> Procedural content generation has become a popular research topic in recent years. However, most content generation systems are specialized to a single game. We are interested in methods that can generate content for a wide variety of games without a game-specific algorithm design. Statistical approaches are a promising avenue for such generators and, more specifically, map generators. In this paper, we explore Markov models as a means of modeling and generating content for multiple domains. We apply our Markov models to Super Mario Bros. , Loderunner , and Kid Icarus in order to determine how well our models perform in terms of the playability of the content generated, the expressive ranges of the models, and the effects of training data on those expressive ranges. <s> BIB016 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Introduction <s> This paper offers an insight of social resiliency in digital games based on ten years of observations from conference presentations on various digital games and virtual reality simulations. The paper articulates the issue of disruptive innovation and its relationship to the driving need for future college and university graduates to be prepared to face challenges associated with complex problems. The paper outlines five key success factors associated with digital game-based learning and their relationship to social resilience. Finally, the paper concludes by discussing several key implications on how future game-based learning designers may be better able to tackle the problem of managing disruptive innovation. Researchers may draw upon this paper’s observations to help frame ways in which future digital games may be better suited for instructional purposes when trying to address challenges of disruptive technology. <s> BIB017 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Introduction <s> This survey explores Procedural Content Generation via Machine Learning (PCGML), defined as the generation of game content using machine learning models trained on existing content. As the importance of PCG for game development increases, researchers explore new avenues for generating high-quality content with or without human involvement; this paper addresses the relatively new paradigm of using machine learning (in contrast with search-based, solver-based, and constructive methods). We focus on what is most often considered functional game content such as platformer levels, game maps, interactive fiction stories, and cards in collectible card games, as opposed to cosmetic content such as sprites and sound effects. In addition to using PCG for autonomous generation, co-creativity, mixed-initiative design, and compression, PCGML is suited for repair, critique, and content analysis because of its focus on modeling existing content. We discuss various data sources and representations that affect the resulting generated content. Multiple PCGML methods are covered, including neural networks, long short-term memory (LSTM) networks, autoencoders, and deep convolutional networks; Markov models, $n$-grams, and multi-dimensional Markov chains; clustering; and matrix factorization. Finally, we discuss open problems in the application of PCGML, including learning from small datasets, lack of training data, multi-layered learning, style-transfer, parameter tuning, and PCG as a game mechanic. <s> BIB018
Digital games have become an interesting topic in the recent years BIB005 , BIB011 . The untapped potency of such digital games is massive, the inherent flexible nature of digital games allows it to convey various messages encompassing multiple disciplines of knowledge  - BIB010 . As technology develops, so does the average computational power of computers  , this is crucial for the computer usage in the sense of processing speed, capacity of data being processed, and optimization of such process. Based on the current state of digital games and its nature, as well as unfathomable speed of how computation power is being developed, the development of game contents is moving at similar pace as well BIB017 . Conventional content generation in digital games is a very rigorous and time-consuming process BIB008 , much of its development pipeline requires multiple parts of development lifecycle and requires multiple expert to validate its output BIB013 . However, with the increase of computational power of computers, an automated process of generating content for digital games emerges, allowing little to no human intervention or interference on the process of generating such contents BIB002 . The means of generating content in digital games requires a set of rules that is defined in a form of algorithm BIB003 , such field is called "procedural content generation" or often abbreviated as PCG BIB012 . As the name suggests, contents are generated procedurally instead of randomly generation with little to no pattern. The usage of PCG is pervasive in modern digital games development, multiple games for entertainment purpose uses procedurally generated content in form of map generation  for strategy games BIB009 as well as generating enemy's formation on role-playing games (RPG) BIB014 . A procedurally generated gameplay area also made by using similar method for puzzle games BIB015 . In fact, the usage of PCG in game development is ubiquitous, not using such method for large-scale games may either be considered a true work of artisanal experts BIB016 , or a bad practice of game development life cycle. However, the usage of PCG on digital games in form of serious games are far less documented. Several studies in form of collective and comprehensive details on PCG has already been done BIB012 , another study has also been done to identify some empirical evidence on how PCG is used in games in general BIB007 as well as a study focusing on its usage in term of game development BIB004 , however the studies done are far too broad while the existing literature review are more on a generic point of view, furthermore, the existing studies that has been done are quite outdated as it is mostly published more than 5 years ago. The domain of PCG is an interesting area in the field of artificial intelligence in game as well as evolutionary computing in general BIB018 , multiple researches on the optimization and usage potency of PCG on multiple fields has already existed-be it related to game or not BIB001 - BIB006 , yet researches on such topics in serious games context in form of PCG application are still scarce. This study aims to address the status quo of PCG usage in serious games, focusing on multiple aspect of PCG as well is how it is implemented, evaluated, and to what kind of games it is implemented on. The contributions of this paper includes: (i) reviewing and eliciting relevant information and inferences regarding the issue on such domain, (ii) providing quick reference on the domain of research being reviewed, and (iii) providing empirical evidence on PCG usage in serious games.