A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Focused Content <s> The volumetric approach for terrain representation is a technique used by several video-games and other graphic applications to manage both surface and geological data from the virtual world. To enhance the exploration experience and due to the amount of data required by this approach, volumetric terrains are usually generated with procedural methods. Nevertheless, one of the main issues of those methods is the generation of cave systems with playable features and a natural appearance. In this paper we propose a new method to generate playable cave systems for 2D and 3D volumetric terrains, based on Voronoi diagrams and Delaunay triangulations. Our approach is completely customizable by the designer by a set of parameters directly related to the cave itself avoiding technical concepts. Additionally, the method runs in a completely independent way, with no interactive steps. <s> BIB001 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Focused Content <s> This paper presents a coevolutionary genetic algorithm for the development of battle formations in Massively Multiplayer Online Real-Time Strategy games. We consider the context of the game Call of Roma, where the battles are turn based and two sides fight each other, each side involving one or more players. The coevolutionary genetic algorithm takes as input a predetermined battle formation, presented by the defense side, and returns a battle formation adapted for the Attack side. The algorithm aims to maximize the battle performance, which is a subjective concept that varies from player to player. The individual encodes the various characteristics of the fighting heroes. We tested the algorithm over a cloud-computing platform, considering test cases modeled by several active players. The results illustrate that the proposed algorithm is able to find a victorious solution for the Attack team, even when it is under unfavorable conditions. <s> BIB002 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Focused Content <s> We experiment with mood-expressing, procedurally generated music for narrative foreshadowing in videogames, investigating the relationship between music and the player's experience of narrative events in a game. We designed and conducted a user study in which the game's music expresses true foreshadowing in some trials (e.g. foreboding music before a negative event) and false foreshadowing in others (e.g. happy music that does not lead to a positive event). We observed players playing the game, recorded analytics data, and had them complete a survey upon completion of the gameplay. Thirty undergraduate and graduate students participated in the study. Statistical analyses suggest that the use of musical cues for narrative foreshadowing induces a better perceived consistency between music and game narrative. Surprisingly, false foreshadowing was found to enhance the player's enjoyment. <s> BIB003 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Focused Content <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> Focused Content <s> Novelty search is a recent algorithm geared toward exploring search spaces without regard to objectives. When the presence of constraints divides a search space into feasible space and infeasible space, interesting implications arise regarding how novelty search explores such spaces. This paper elaborates on the problem of constrained novelty search and proposes two novelty search algorithms which search within both the feasible and the infeasible space. Inspired by the FI-2pop genetic algorithm, both algorithms maintain and evolve two separate populations, one with feasible and one with infeasible individuals, while each population can use its own selection method. The proposed algorithms are applied to the problem of generating diverse but playable game levels, which is representative of the larger problem of procedural game content generation. Results show that the two-population constrained novelty search methods can create, under certain conditions, larger and more diverse sets of feasible game levels than current methods of novelty search, whether constrained or unconstrained. However, the best algorithm is contingent on the particularities of the search space and the genetic operators used. Additionally, the proposed enhancement of offspring boosting is shown to enhance performance in all cases of two-population novelty search. <s> BIB005 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Focused Content <s> Traditionally a dedicated level designer has to create every aspect of a level by hand, distribute and gather people to test it. Especially for multiplayer maps, the cyclic process of developing and testing can be quite cumbersome and time consuming. This paper presents a novel approach to provide live generation of levels for such multiplayer maps through procedural content generation and interactive evolution. The approach is demonstrated in the FPSEvolver video game that aims at replicating the look and feel of popular games like Counter-Strike. Without leaving the game a group of players can generate, play and improve levels to fit their particular preferences by voting on a selection of evolving levels. This approach focuses on maintaining the level design principles for modern first-person shooters that encourages good engagements between players, in the popular game mode “bomb defusal”. The presented approach can generate enjoyable maps that adapt to the preferences of players across a range of skill levels. Several distinct types of levels were created during testing, which range from open to closed, and complex to simple, each fitting closely with what the different players consider a good map. <s> BIB006 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Focused Content <s> Digital games often center on a series of challenges designed to vary in difficulty over the course of the game. Designers, however, lack ways to ensure challenges are suitably tailored to the abilities of each game player, often resulting in player boredom or frustration. Challenge tailoring refers to the general problem of matching designer-intended challenges to player abilities. We present an approach to predict temporal player performance and select appropriate content to solve the challenge tailoring problem. Our temporal collaborative filtering approach—tensor factorization—captures similarities among players and the challenges they face to predict player performance on unseen, future challenges. Tensor factorization accounts for varying player abilities over time and is a generic approach capable of modeling many kinds of players. We use constraint solving to optimize content selection to match player skills to a designer-specified level of performance and present a model—performance curves—for designers to specify desired, temporally changing player behavior. We evaluate our approach in a role-playing game through two empirical studies of humans and one study using simulated agents. Our studies show tensor factorization scales in multiple game-relevant data dimensions, can be used for modestly effective game adaptation, and can predict divergent player learning trends. <s> BIB007 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Focused Content <s> In this paper, the quest for the perfect perfect-maze is performed over the search space of perfect mazes using an approach of search-based procedural content generation. Perfect maze construction is rather random with little to no control of the final product. We propose a search-based framework based on attributes or metrics of a constructed maze to provide a foundation for evaluation functions (fitness functions). Since the meaning of “perfect” is subjective and different for every designer, we allow designers to construct their own evaluation function to generate the best maze. We have also analyzed each metric's space on an exhaustive enumeration of small-sized mazes to determine allowable, and perhaps desirable, ranges for each metric. Using these metrics, an evaluation function is constructed to search for the “best” maze. <s> BIB008 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Focused Content <s> Abstract TrackGen is an online tool for the generation of tracks for two open-source 3D car racing games (TORCS and Speed Dreams). It integrates interactive evolution with procedural content generation and comprises two components: (i) a web frontend that maintains the database of all the evolved populations and manages the interaction with users (by collecting users evaluations and providing access to the evolved tracks) and (ii) an evolutionary/content-generation backend that runs both the evolutionary algorithm and generates the actual game content that is available through the web frontend. The first prototype of the tool was presented in July 2011 but advertised only to researchers; the first official version which generated tracks only for TORCS was released to the game community in September 2011; due to the many requests, we released a new version soon afterwards, in January 2012, with support for Speed Dreams (the fork of TORCS focused on visual realism and graphic quality) that has been online since then. From January 2012 until July 2014, TrackGen had more than 7600 unique visitors who visited the website around 11,500 times and viewed 85,500 pages; it was employed to evolve more than 8853 tracks, and it was used to download 12,218 tracks. Some of the tracks evolve by our system have been also included in the TORCS distribution. <s> BIB009 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Focused Content <s> In this paper, we propose a hybrid method to procedurally generate terrains in Scorch Earth. It features both the fast computing speed of random techniques and the wide variety of details of fractal techniques. We indirectly evaluate terrains by analyzing the gameplay data. We create an AI agent to model players' behaviors to produce gameplay data. The agent is capable to shoot and move in different scenarios. Finally we introduce metrics for gameplays to select good terrains. <s> BIB010 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Focused Content <s> This paper presents a novel approach to procedural generation of urban maps for First Person Shooter (FPS) games. A multi-agent evolutionary system is employed to place streets, buildings and other items inside the Unity3D game engine, resulting in playable video game levels. A computational agent is trained using machine learning techniques to capture the intent of the game designer as part of the multi-agent system, and to enable a semi-automated aesthetic selection for the underlying genetic algorithm. <s> BIB011 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Focused Content <s> The recent interest in procedural content generation for video games has created the need to establish techniques for assessment of generated content. We present an investigation into the factors determining perceived difficulty in procedurally generated game levels. In doing so, an approach to identify relevant factors pertaining to player experience is established, which is subsequently used in the development of predictive difficulty models. In this paper, we apply our methodology to the genre of 2D platformers, presenting an investigation into factors related to difficulty, the development of a test-bed that can be used to collect the data, data collection and subsequent analysis. We investigate the contribution of the identified game and player metrics towards predicting difficulty using Multi-Layer Perceptron, J48 and Random Forest classifiers from WEKA. This work is presented as a preliminary investigation into modeling difficulty from procedural content. Significantly, this investigation provides a preliminary insight into metrics that can be used for developing a classification model for perceived difficulty. <s> BIB012 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Focused Content <s> Real-time adaptation of computer games' content to the users' skills and abilities can enhance the player's engagement and immersion. Understanding of the user's potential while playing is of high importance in order to allow the successful procedural generation of user-tailored content. We investigate how player models can be created in car racing games. Our user model uses a combination of data from unobtrusive sensors, while the user is playing a car racing simulator. It extracts features through machine learning techniques, which are then used to comprehend the user's gameplay, by utilising the educational theoretical frameworks of the Concept of Flow and Zone of Proximal Development. The end result is to provide at a next stage a new track that fits to the user needs, which aids both the training of the driver and their engagement in the game. In order to validate that the system is designing personalised tracks, we associated the average performance from 41 users that played the game, with the difficulty factor of the generated track. In addition, the variation in paths of the implemented tracks between users provides a good indicator for the suitability of the system. <s> BIB013 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Focused Content <s> Procedural content generation has been a major part of computer gaming since the early 1980s. Because of the limited memory in early computers and video game consoles, procedural generation was used to provide a player with a wide variety of different levels without each level needing to be pre-built and saved. ::: ::: This study improves and expands upon algorithms created by Mike Anderson and the Future Data Labs. The goal is to devise a general, globalized algorithm that produces a randomly generated multi-dimensional, non-Euclidean map based on user-provided parameters. ::: ::: These maps will then be evaluated by an updated set of Anderson's original acceptance requirements and compared to his original algorithm on the following metrics to determine if it is indeed an improvement. ::: ::: The final results obtained show a considerable decrease in the number of dead ends, a considerable increase in the number of alternative paths to the exit and an increase in the length of the alternative paths to the goal/end. Additionally, these objectives were achieved with a reasonable increase in time cost. <s> BIB014 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Focused Content <s> Abstract The current paper investigates multiple approaches to modeling human decision making styles for procedural play-testing. Building on decision and persona theory we evolve game playing agents representing human decision making styles. Three kinds of agents are evolved from the same representation: procedural personas, evolved from game designer expert knowledge, clones, evolved from observations of human play and aimed at general behavioral replication, and specialized agents, also evolved from observation, but aimed at determining the maximal behavioral replication ability of the representation. These three methods are then compared on their ability to represent individual human decision makers. Comparisons are conducted using three different proposed metrics that address the problem of matching decisions at the action, tactical, and strategic levels. Results indicate that a small gallery of personas evolved from designer intuitions can capture human decision making styles equally well as clones evolved from human play-traces for the testbed game MiniDungeons. <s> BIB015 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Focused Content <s> This paper describes a case-study on the use of AGE, the Authorial Game Evolution approach, a creativity support tool designed to assist game designers. AGE allows designers to conduct a systematic process of generation and evaluation of game-prototypes, as well as automatically evolve a game-prototype until it mediates a desired form of game-play experience. To assess the tool, a design case study was held where a designer used AGE to create a game. We used a convergent mixed methods experimental design, and analysed quantitative and qualitative data resulting from four design sessions. Creativity Support Index self-report shows the designer found AGE very good in supporting his design, especially for exploration of the design-space. However, he appropriated it exclusively for exploration, not optimization. These show AGE has potential for exploring the design-space, though issues remain before it is an effective medium for high-quality designs. <s> BIB016 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Focused Content <s> Maze running games represent a popular genre of video games and the design of playable mazes provides an interesting research challenge in procedural content generation for computational intelligence research in games. In this paper, we attack the problem of creating playable mazes by using genetic algorithms to evolve cellular automata rules that lead to playable mazes. More specifically, a fixed number of evolved-rule applications generates maze like patterns on a cellular automata grid and a region merging algorithm then generates the final, playable maze. Since maze path lengths correlate with maze playability, the genetic algorithm searches for cellular automata rules that lead to longer path lengths. Results from two types of cellular automata and three different fitness functions of path length show that our approach results in a variety of interesting, playable mazes with longer path lengths and complex paths. <s> BIB017 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Focused Content <s> A road network is one of the core elements of urban environments, strongly defining their layout. Procedural modeling has been increasingly used to create such road networks. However, many procedural methods are complex and difficult to master by non-experts, often have a limited and hard-to-control expressive range, and require a variety of specialized input data to generate a complex road network. To mitigate this, some methods proposed to use stochastic data on road patches extracted from example maps to design a road network following a given urban style. We propose a novel patch-based method that uses the semantics of individual patches to help guiding the procedural generation. Our approach combines the advantages of patch-based generation with those of conventional parametric methods. Due to the intuitive character of semantic parameters and tags, our approach provides for an easy customization of fictive road network creation, allowing a user to easily define various types of road network styles, containing only the desired features and structures of real-world road networks. <s> BIB018 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Focused Content <s> Automatically generating content for videogames has long been a staple of game development and the focus of much successful research. Such forays into content generation usually concern themselves with producing a specific game component, such as a level design. This has proven a rich and challenging area of research, but in focusing on creating separate parts of a larger game, we miss out on the most challenging and interesting aspects of game development. By expanding our scope to the automated design of entire games, we can investigate the relationship between the different creative tasks undertaken in game development, tackle the higher level creative challenges of game design, and ultimately build systems capable of much greater novelty, surprise, and quality in their output. This paper, the first in a series of two, describes two case studies in automating game design, proposing cooperative coevolution as a useful technique to use within systems that automate this process. We show how this technique allows essentially separate content generators to produce content that complements each other. We also describe systems that have used this to design games with subtle emergent effects. After introducing the technique and its technical basis in this paper, in the second paper in the series we discuss higher level issues in automated game design, such as potential overlap with computational creativity and the issue of evaluation. <s> BIB019 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Focused Content <s> Procedural content generation is generally viewed as a means to an end—a tool employed by designers to overcome technical problems or achieve a particular design goal. When we move from generating single parts of games to automating the entirety of their design, however, we find ourselves facing a far wider and more interesting set of problems than mere generation. When the designer of a game is a piece of software, we face questions about what it means to be a designer, about computational creativity, and about how to assess the growth of these automated game designers and the value of their output. Answering these questions can lead to new ideas in how to generate content procedurally, and produce systems that can further the cutting edge of game design. This paper describes work done to take an automated game designer and advance it towards being a member of a creative community. We outline extensions made to the system to give it more autonomy and creative independence, in order to strengthen claims that the software is acting creatively. We describe and reflect upon the software’s participation in the games community, including entering two game development contests, and show the opportunities and difficulties of such engagement. We consider methods for evaluating automated game designers as creative entities, and underline the need for automated game design to be a major frontier in future games research. <s> BIB020 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Focused Content <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> BIB021 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Focused Content <s> 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. <s> BIB022 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Focused Content <s> Tarot has been used for centuries as a method to give structure to storytelling, both in game and fortune-telling settings. As such, tarot cards have developed over time, expanding the symbolism and depth of meaning associated with each card. This provides a corpus for a large number of possible stories, making tarot a rich area of exploration for story generation. Therefore, we have created a tarot-based narrative generation system that creates short movie-like story synopses, along with a tagline one might see on a movie poster. This project is in early development; we have created a prototype as a proof of concept. The project exists as a webpage that an interactor can use to draw new tarot cards for the story spread (card layout) and generate new stories from them. In this paper we discuss the details of our system and describe more details about the tarot as a corpus. We also discuss future areas of exploration based on our proof of concept. <s> BIB023 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Focused Content <s> This paper introduces a fully automatic method for generating video game tutorials. The AtDELFI system (Automatically DEsigning Legible, Full Instructions for games) was created to investigate procedural generation of instructions that teach players how to play video games. We present a representation of game rules and mechanics using a graph system as well as a tutorial generation method that uses said graph representation. We demonstrate the concept by testing it on games within the General Video Game Artificial Intelligence (GVG-AI) framework; the paper discusses tutorials generated for eight different games. Our findings suggest that a graph representation scheme works well for simple arcade style games such as Space Invaders and Pacman, but it appears that tutorials for more complex games might require higher-level understanding of the game than just single mechanics. <s> BIB024
This part of the result sub-chapter answers RQ6: What content being focused are being researched based on the existing researches? Figure 5 . Total number of papers based on its focused content. PCG focused contents are defined as the contents which PCG approach is being adapted. In this context, PCG are being adapted into contents whether it is into a form of game, or when the work is not creating a game as a testbed. Based on the figure 5, a vast majority of the research are focused on level generation, which is not a surprise considering PCG are most suitable to create variations of playable level inside a game, apart of that, general content and artificial intelligence are also an emerging content being focused in the current state of researches. Several minor contents such as quest generation, virtual world objects, as well as other type of contents are also included in this manner. Focusing more on the level generation focused contents, multiple works are done to either create the contents by using PCG BIB004 , BIB011 , BIB005 , BIB006 - BIB022 , thus automating the content generation in the game in term of its physical contents. While several other works improve such content generation into the logical contents of the game by focusing into its game difficulty BIB012 - BIB007 , creating an adaptive and adjustable level generation personalized to each unique user. Multiple level generation focused contents tend to alter the map creation process in form of either a platform where the player plays the game such as mazes BIB008 , BIB017 , cave systems BIB001 , or tracks of traversable areas BIB013 , BIB009 , BIB018 . Another result also shows map generation focused on games that involve procedurally generated dungeon mechanism [62]- BIB014 which makes an elaborate level multi-level playable area based on a particular game genre/mechanics. In term of A.I., PCG are used intensively to create an adaptable A.I. that would adapt its behavior based on user decision, hence creating a dynamic environment similar to the level generation, but instead is adapted to the opposing player in the game instead of altering the playable area BIB015 , this adaptation of PCG into A.I. are also adapted into a more extensive A.I. that even can create its own game BIB019 , BIB020 , such as related to previous sub-chapter of work type where the generator generates an entire game instead of contents inside a game. The adaptation of PCG into A.I. are mostly targeted to learn the player pattern and create challenge to the player based on its unique activity, creating a different playing experience for each players BIB021 , BIB002 , as well as using multiple player activity to evolve the A.I. to be able to do more meaningful decision BIB010 . A more general content being generated by using PCG is categorized as "content" on the figure as it does not focus on a single content but rather creating an entire system of its own as an output BIB016 . In term of quest focused content, several form of PCG has been known to be able to create a set of narration and stories BIB023 ,  , while other contents are more niche such as generating music BIB003 or instructions for players BIB024 .