We propose the use of quality-diversity algorithms for mixed-initiative game content generation. This idea is implemented as a new feature of the Evolutionary Dungeon Designer, a system for mixed-initiative design of the type of levels you typically find in computer role playing games. The feature uses the MAP-Elites algorithm, an illumination algorithm which divides the population into a number of cells depending on their values along several behavioral dimensions. Users can flexibly and dynamically choose relevant dimensions of variation, and incorporate suggestions produced by the algorithm in their map designs. At the same time, any modifications performed by the human feed back into MAP-Elites, and are used to generate further suggestions.
Procedural content generation (PCG) can be a useful tool for aiding creativity and efficiency in the process of designing game levels. Mixed-initiative level generation tools where a designer and an algorithm collaborate to iteratively generate game levels have been used for this purpose -taking advantage of the combination of computational efficiency and human intuition and creativity. However, it can be difficult for designers to work with tools that do not respond to the common language of games: game design patterns.It has been demonstrated that game design patterns can be integrated into PCG algorithms, but formally-defined and hierarchically-arranged game design patterns have not yet been used as a means of increasing gameplay-based control in mixedinitiative dungeon generators. We present a method for evolving dungeon rooms using multi-level game design patterns in the objective function of a genetic algorithm, as well as an instantiation of this method in a mixed-initiative dungeon design tool. Our results show that we are able to control the frequency and type of design patterns in generated rooms using pattern-related input parameters, enabling us to create dungeon rooms containing a wide variety of patterns on different levels of abstraction.Results from a small-scale user study of professional game developers suggest that the use of game design patterns in mixed-initiative level design tools can be a promising way of providing a good starting point when designing a level, as well as offering meaningful gameplay related feedback throughout the design process. We also identify challenges that will need to be faced if game design pattern-based mixed-initiative level design tools are to become a part of the game designer's toolkit. Popular Science SummaryModern video game development is time-consuming and costly. As games become larger and consumers expect them to contain varied and frequently updated content, the burden on the creators of this content steadily increases. In the 1980s, games like Rogue and Elite pioneered algorithmic approaches to the automatic creation of game content, known as procedural content generation (PCG), allowing for game worlds vastly larger or more varied than those their developers could have created by hand. Since then, whole genres like the Rogue-inspired roguelike have been built around PCG and algorithmic approaches to game content generation are common in many areas of game development.Recently, researchers have begun to explore the potential of PCG techniques as an aid to human game designers, rather than as purely automated processes. Combining the time-saving and raw computational power of PCG with the intuition and creativity of humans might allow us to take the best of both worlds and produce higher quality game content faster.An open question that we address in this thesis is how best to facilitate the collaboration between human designers and PCG algorithms in the domain of game level generation. We argue that, in order to meet this goal, level generatio...
Mixed-initiative systems highlight the collaboration between humans and computers in fostering the generation of more interesting content in game design. In light of the ever-increasing cost of game development, providing mixed-initiative tools can not only significantly reduce the cost but also encourage more creativity amongst game designers. The Evolutionary Dungeon Designer (EDD) [3] is a mixed-initiative tool with a focus on using evolutionary computation to procedurally generate content that adhere to game design patterns. As part of an ongoing project, feedback from a user study on EDD's capabilities as a mixed-initiative design tool pointed out the need for improvement on the tool's functionalities [4]. In this paper we present a review of the principles of the mixedinitiative model, as well as the existing approaches that implement it. The outcome of this analysis allows us to address the appointed needs for improvement by shaping a new version of EDD that we describe here. Finally, we also present the results from a user study carried out with professional game developers, in order to assess EDD's new functionalities. Results show an overall positive evaluation of the tool's intuitiveness and capabilities for empowering game developers' creative skills during the design process of dungeons for adventure games. They also allow us to identify upcoming challenges pattern-based mixed-initiative tools could benefit from. CCS CONCEPTS • Theory of computation → Evolutionary algorithms; • Applied computing → Computer games; • Software and its engineering → Interactive games;
The Evolutionary Dungeon Designer (EDD) [1] is as a mixedinitiative tool for creating dungeons for adventure games. Results from a user study with game developers positively evaluated EDD as a suitable framework for collaboration between human designers and PCG suggestions, highlighting these as time-saving and inspiring for creating dungeons [2]. Previous work on EDD identified the need of assessing aesthetic criteria as a key area for improvement in its PCG Engine. By upgrading the individual encoding system and the fitness evaluation in EDD's evolutionary algorithm, we present three techniques to preserve and account the designer's aesthetic criteria during the dungeon generation process: the capability of locking sections for preserving custom aesthetic structures, as well as the measurement of symmetry and similarity in the provided suggestions. CCS CONCEPTS • Theory of computation → Evolutionary algorithms; • Applied computing → Computer games; • Software and its engineering → Interactive games;
We propose the Interactive Constrained MAP-Elites, a quality-diversity solution for game content generation, implemented as a new feature of the Evolutionary Dungeon Designer: a mixed-initiative co-creativity tool for designing dungeons. The feature uses the MAP-Elites algorithm, an illumination algorithm that segregates the population among several cells depending on their scores with respect to different behavioral dimensions. Users can flexibly and dynamically alternate between these dimensions anytime, thus guiding the evolutionary process in an intuitive way, and then incorporate suggestions produced by the algorithm in their room designs. At the same time, any modifications performed by the human user will feed back into MAP-Elites, closing a circular workflow of constant mutual inspiration. This paper presents the algorithm followed by an in-depth analysis of its behaviour, with the aims of evaluating the expressive range of all possible dimension combinations in several scenarios, as well as discussing their influence in the fitness landscape and in the overall performance of the mixed-initiative procedural content generation.
This paper presents the Designer Preference Model, a datadriven solution that pursues to learn from user generated data in a Quality-Diversity Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the user's design style to better assess the tool's procedurally generated content with respect to that user's preferences. Through this approach, we aim for increasing the user's agency over the generated content in a way that neither stalls the user-tool reciprocal stimuli loop nor fatigues the user with periodical suggestion handpicking. We describe the details of this novel solution, as well as its implementation in the MI-CC tool the Evolutionary Dungeon Designer. We present and discuss our findings out of the initial tests carried out, spotting the open challenges for this combined line of research that integrates MI-CC with Procedural Content Generation through Machine Learning.
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