A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Research gap <s> In the present work, we tackle the issue of designing, prototyping and testing a general-purpose automated level editor for platform video games. Beside relieving level designers from the burden of repetitive work, Procedural Content Generation can be exploited for optimizing the development process, increasing re-playability, adapting games to specific audiences, and enabling new games mechanics. The tool proposed in this paper is aimed at producing levels that are both playable and fun. At the same time, it should guarantee maximum freedom to the level designer, and suggest corrections functional to the quality of the player experience. <s> BIB001 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Research gap <s> Procedural content generation (PCG) has recently become one of the hottest topics in computational intelligence and AI game research. While some substantial progress has been made in this area, there are still several challenges ranging from content evaluation to personalized content generation. In this paper, we present a novel PCG framework based on machine learning, named learning-based procedure content generation (LBPCG), to tackle a number of challenging problems. By exploring and exploiting information gained in game development and public player test, our framework can generate robust content adaptable to end-user or target players on-line with minimal interruption to their gameplay experience. As the data-driven methodology is emphasized in our framework, we develop learning-based enabling techniques to implement the various models required in our framework. For a proof of concept, we have developed a prototype based on the classic open source first-person shooter game, Quake. Simulation results suggest that our framework is promising in generating quality content. <s> BIB002 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Research gap <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> BIB003 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Research gap <s> Abstract Current video games use simple methods to deal with interactive narratives and the enormous variety of player types. In this paper, we propose a novel approach to interactive storytelling in games, in which the quests and the ongoing story are determined in view of individual personality traits and behavioral attitudes in a nondeterministic way. Our method starts the process employing a new technique to assess the player’s personality traits according to the well-known Big Five model. These traits are then used by a nondeterministic planning algorithm to define adaptive goal hierarchies. In addition, an artificial neural network is trained to predict player behaviors in real-time, allowing partial-order planning operators to use player behaviors and personality traits as logical terms in their preconditions. With this approach, a richer individualized experience is provided to the player, while preserving consistency with the conventions of the chosen genre. <s> BIB004 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Research gap <s> Current research on adaptive games has mainly focused on adjusting difficulty in a variety of ways, for example, by providing some control over adaptive game world generation. These methods, however, are mostly ad-hoc and require quite some technical skills. To the best of our knowledge, so far, there has been no adaptive method that is truly generic and explicitly designed to actively include game designers in the content creation loop. In this paper, we introduce a generic method that enables designers to author adaptivity of game world generation, in a very expressive and specific fashion. Our approach uses adaptation rules , which build atop gameplay semantics in order to steer the online generation of the game content. Designers create these rules by associating skill profiles , describing skill proficiency, with content descriptions , detailing the desired properties of the specific game world content. This game content is then generated online using a matching and retrieval approach. We performed user studies with both designers and players and concluded that adaptation rules provide game designers with a rich expressive range to effectively convey specific adaptive gameplay experiences to players. <s> BIB005 </s> A literature review of Empirical Evidence on Procedural Content Generation in Game-Related Implementation <s> Research gap <s> Procedural story generation has become an important research area due to its attractiveness to computer entertainment applications. Several approaches have been proposed to create procedural stories for games based mainly on logic programming and decision trees. These approaches use a set of possible events that are executed based on how the player interacts with the story. However, these approaches commonly use a fixed set of characters with predefined relationships. In contrast, in this article, we propose an approach that creates dynamic stories based on two networks: a handcrafted event network and a dynamic artificial social network created for each new story. This allows each story to have its own set of characters with a unique relationship network, resulting in an even wider range of possible stories. We demonstrate this with some examples and discuss how this approach can be implemented for interactive computer entertainment systems, such as video games. <s> BIB006
Based on the result stated previously, there exist several research gaps that can be inferred from aforementioned results. In this paper, research gaps are derived from the inference of specific set of papers from the research questions. Such inference is done based on similarity of topics being researched either from the domain researched or the type of PCG being done. On a specific domain of serious games, papers with clear game-related contents are more focused on generating contents inside the game instead of using PCG as the main method of creating the game, with a little amount of paper using PCG as a testbed to prove their results BIB003 , BIB001 , BIB002 . With most paper focusing on level generation among all types of content generation, a gap can be inferred that more niche usage of PCG for contents that are not level generation are still uncommon, albeit it is understandable why PCG on level generation is to be expected. Another gap is shown on the application of PCG based on its game genre and game model, action game genre and platforming game model are more prevalent compared to other game models and genres. More on the focused content, as mentioned before, level generation are the most prevalent type of content being focused on PCG researches based on paper found, however, several other contents such as quests BIB004 and world objects BIB005 , BIB006 also exist to a certain extent, as such, any form of non-level generation PCG would have a rather large novelty compared to level generation.