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Abstract: "Procedural content generation (PCG) is concerned with automatically generating game content, such as levels, rules, textures and items. But could the content generator itself be seen as content, and thus generated automatically? This would be very useful if one wanted to avoid writing a content generator for a new game, or if one wanted to create a content generator that generates an arbitrary amount of content with a particular style or theme. In this paper, we present a procedural procedural level generator generator for Super Mario Bros. It is an interactive evolutionary algorithm that evolves agentbased level generators. The human user makes the aesthetic judgment on what generators to prefer, based on several views of the generated levels including a possibility to play them, and a simulation-based estimate of the playability of the levels. We investigate the characteristics of the generated levels, and to what extent there is similarity or dissimilarity between levels and between generators."

"This dissertation presents the use of procedural content generation to create expressive design tools: content generators that are accessible to designers, supporting the creation of new kinds of design tools and enabling the exploration of a new genre of game involving the deep integration of procedural content generation into game mechanics and aesthetics. The first of these tools is Tanagra, the first ever AI-assisted level design tool that supports a designer creating levels for 2D platforming games. Tanagra guarantees that levels created in the tool are playable, and provides the designer with the ability to modify generated levels and directly control level pacing. The second tool is Launchpad, which supports a designer controlling both component and pacing features of generated levels; its companion game Endless Web uses the generator to create an infinite world for players to explore and alter through their choices. Endless Web is one of a handful of games in a new genre enabled by content generation: PCG-based games. Finally, this dissertation presents a novel method for understanding, visualizing, and comparing a generator's expressive range, thus allowing designers to understand the implications of decisions they will make during the design process."

"This paper describes the creation of the game Endless Web, a 2D platforming game in which the player's actions determine the ongoing creation of the world she is exploring. Endless Web is an example of a PCG-based game: it uses procedural content generation (PCG) as a mechanic, and its PCG system, Launchpad, greatly influenced the aesthetics of the game. All of the player's strategies for the game revolve around the use of procedural content generation. Many design challenges were encountered in the design and creation of Endless Web, for both the game and modifications that had to be made to Launchpad. These challenges arise largely from a loss of fine-grained control over the player's experience; instead of being able to carefully craft each element the player can interact with, the designer must instead craft algorithms to produce a range of content the player might experience. In this paper we provide a definition of PCG-based game design and describe the challenges faced in creating a PCG-based game. We offer our solutions, which impacted both the game and the underlying level generator, and identify issues which may be particularly important as this area matures."

Abstract: "Procedural content generation and design patterns could potentially be combined in several dierent ways in game design. This paper discusses how to combine the two, using automatic platform game level design as an example. The paper also present work towards a pattern-based level generator for Super Mario Bros, namely an analysis of the levels of the original Super Mario Bros game into 23 dierent patterns."

Abstract :"Procedural content generation (PCG), the algorithmic creation of game content with limited or indirect user input, has much to offer to game design. In recent years, it has become a mainstay of game AI, with significant research being put towards the investigation of new PCG systems, algorithms, and techniques. But for PCG to be absorbed into the practice of game design, it must be contextualised within design-centric as opposed to AI or engineering perspectives. We therefore provide a set of design metaphors for understanding potential relationships between a designer and PCG. These metaphors are: TOOL, MATERIAL, DESIGNER, and DOMAIN EXPERT. By examining PCG through these metaphors, we gain the ability to articulate qualities, consequences, affordances, and limitations of existing PCG approaches in relation to design. These metaphors are intended both to aid designers in understanding and appropriating PCG for their own contexts, and to advance PCG research by highlighting the assumptions implicit in existing systems and discourse"

Abstract: "Search-based procedural content generation methods allow video games to introduce new content continually, thereby engaging the player for a longer time while reducing the burden on developers. However, games so far have not explored the potential economic value of unique evolved artifacts. Building on this insight, this paper presents for the first time a Facebook game called Petalz in which players can share flowers they breed themselves with other players through a global marketplace. In particular, the market in this social game allows players to set the price of their evolved aestheticallypleasing flowers in virtual currency. Furthermore, the transaction in which one player buys seeds from another creates a new social element that links the players in the transaction. The combination of unique user-generated content and social gaming in Petalz facilitates meaningful collaboration between users, positively influences the dynamics of the game, and opens new possibilities in digital entertainment."

Abstract: "We define data games as games where gameplay and/or game content is based on real-world data external to the game, and where gameplay supports the exploration of and learning from this data. This concept is discussed in rela- tion to open data, procedural content generation and serious games, and research challenges are outlined. To illustrate the concept, we present six prototype games and content generators of our own making. We also present a tentative taxonomy of actual and potential data games, and situate the described games within this taxonomy."

Abstract: "This paper discusses how to use design patterns in procedural level generation, with particular reference to the classic console game Super Mario Bros. In a previous paper, we analyzed the levels in this game to nd a set of recurring level design patterns, and discussed an implementation where levels were produced from concatenation of these patterns. In this paper, we instead propose using patterns as design objectives. An implementation of this based on evolutionary computation is presented. In this implementation, levels are represented as a set of vertical slices from the original game, and the tness function count the number of patterns found. Qualitative analysis of generated levels is performed in order to identify strengths and challenges of this method"

From the abstract: "In this paper, we propose the integration of two PCG-based approaches (experiencedriven and context-driven PCG) to support the generation of adaptive mobile game levels. We present and discuss the implementation of our approach in an existing game, 7's Wild Ride. Gameplay semantics and player modeling are used to steer a level generator, featuring a time-dependent dynamic diculty adjustment mechanism. From our two user studies, we conclude that (i) context-driven levels are preferable over traditional ones, and (ii) the game can adapt to dierent player types, keeping its gameplay balanced and player satisfaction."

Abstract: "In this project, the following research question is set forth: is it possible to create fair maps for a video game using multi objective evolution algorithms? A description of the video game used for this project, Civilization V, is provided as well as an overview of other map generation methods, and research being done in the field of procedural content generation. A definition for what is fair is made and expressed through functions, that evaluate maps for the video game Civilization V. These evaluation functions express five distinct perspectives on how fair maps are perceived. The fitness functions are designed to conflict as little as possible with each other. A method is defined as to how this theory is applied in practice to generate maps for Civilization V. The evaluation functions are applied on maps from the game's map generation method, and compared to maps that have been evolved with the method provided by this project."

Abstract: "Procedural generation of games has become an active re- search eld. We present a system that automatically gen- erates an interactive ction (IF) by learning from crowd- sourced corpora of example stories. We ask crowd workers from Amazon Mechanical Turk to write short stories about a given situation with simple language, from which a plot graph is learned, containing plot events, temporal prece- dence and mutual exclusion relations between the events. The plot graph describes an IF where players and non-player characters choose from executable events as determined by the plot graph. We demonstrate an IF learned from the domain of bank robbery"

Abstract: "We introduce Mechanic Miner, an evolutionary system for discovering simple two-state game mechanics for puzzle platform games. We demonstrate how a re ection-driven generation technique can use a simulation of gameplay to select good mechanics, and how the simulation- driven process can be inverted to produce challenging levels specic to a generated mechanic. We give examples of levels and mechanics generated by the system, summarise a small pilot study conducted with example levels and mechanics, and point to further applications of the technique, including applications to automated game design."

Abstract: "In this paper, we describe a methodology for capturing player experience while interacting with a game and we present a data-driven approach for modeling this interaction. We believe the best way to adapt games to a specic player is to use quantitative models of player ex- perience derived from the in-game interaction. Therefore, we rely on crowd-sourced data collected about game context, players behavior and players self-reports of dierent aective states. Based on this informa- tion, we construct estimators of player experience using neuroevolution- ary preference learning. We present the experimental setup and the re- sults obtained from a recent case study where accurate estimators were constructed based on information collected from players playing a rst- person shooter game. The framework presented is part of a bigger picture where the generated models are utilized to tailor content generation to particular player's needs and playing characteristics."

"Two-Population Novelty Search for Generating Game Content"

Automating game content generation

Abstract: "Abstract-In computer games and simulations, content is often rather static and rigid. As a result, its prescripted nature can lead to predictable and impersonal gameplay, while alienating unconventional players. Adaptivity in games has therefore been recently proposed to overcome these shortcomings and make games more challenging and appealing. In this paper, we survey present research on game adaptivity, identifying, and discussing the main challenges, and pointing out some of the most promising directions ahead.We first survey the purposes of adaptivity, as the principles that could steer an adaptation and generation engine. From this perspective, we proceed to thoroughly discuss adaptivity's targets and methods. Current advances and successes in this emerging field point to many yet unexplored research opportunities. Among them, we discuss the use of gameplay expectations, learning preferences, and assessment data in the integrated adaptation of game worlds, scenarios, and quests. We conclude that, among other methods, procedural content generation and semantic modeling can powerfully combine to create offline customized content and online adjustments to game worlds, scenarios, and quests. These and other promising methods, deserving ample research efforts, can therefore, be expected to significantly contribute towards making games and simulations even more unpredictable, effective, and fun."

From the abstract: "Traditionally, the tasks associated with game AI revolved around non player character (NPC) behavior at dierent levels of control, varying from navigation and pathnding to decision making. Commercial-standard games developed over the last 15 years and current game productions, however, suggest that the traditional challenges of game AI have been well addressed via the use of sophisticated AI approaches, not necessarily following or inspired by advances in academic practices. The marginal penetration of traditional academic game AI methods in industrial productions has been mainly due to the lack of constructive communication between academia and industry in the early days of academic game AI, and the inability of academic game AI to propose methods that would signicantly advance existing development processes or provide scalable solutions to real world problems. Recently, however, there has been a shift of research focus as the current plethora of AI uses in games is breaking the non-player character AI tradition. A number of those alternative AI uses have already shown a signicant potential for the design of better games. This paper presents four key game AI research areas that are currently reshaping the research roadmap in the game AI eld and evidently put the game AI term under a new perspective. These game AI agship research areas include the computational modeling of player experience, the procedural generation of content, the mining of player data on massive-scale and the alternative AI research foci for enhancing NPC capabilities."