As of 2023, education surrounding game design has become a fixture in university education systems around the world. As the teaching of game design is an inherently interdisciplinary subject with many connections to the arts and humanities, there are a diverse range of perspectives as to what the focus of each curriculum should include. In this essay, we argue that game programs with a more technical focus should include both design and play-focused approaches embedded into the pedagogy. We present two case studies drawing from our education journeys studying games as well as our experiences teaching in both game and computer science programs, and discuss the resulting benefits of integrating these concepts into our practice.

Elements of randomness are a very common factor in modern digital games, from simple rolls of a die to complex AI systems. These elements have an impact on how the player experiences a game. We believe that exploring the field of luck analysis can benefit designers through a developed understanding of how such elements affect players. In this study, we explore how elements of randomness affect players in the roguelike deckbuilding game, Slay the Spire using data clustering. Three player skill groups were identified with the use of clustering: Winners, Low skill losers and High skill losers. Our results indicate that people who succeeded in beating the game, had an increased amount of randomness in the form of cards by a factor of 1.82. Showing that more skilled players do not shy away from randomness but instead embrace it more than lower skilled players.

Narratives are a predominant part of games, and their design poses challenges when identifying, encoding, interpreting, evaluating, and generating them. One way to address this would be to approach narrative design in a more abstract layer, such as narrative structures. This paper presents Story Designer, a mixed-initiative co-creative narrative structure tool built on top of the Evolutionary Dungeon Designer (EDD) that uses tropes, narrative conventions found across many media types, to design these structures. Story Designer uses tropes as building blocks for narrative designers to compose complete narrative structures by interconnecting them in graph structures called narrative graphs. Our mixed-initiative approach lets designers manually create their narrative graphs and feeds an underlying evolutionary algorithm with those, creating quality-diverse suggestions using MAP-Elites. Suggestions are visually represented for designers to compare and evaluate and can then be incorporated into the design for further manual editions. At the same time, we use the levels designed within EDD as constraints for the narrative structure, intertwining both level design and narrative. We evaluate the impact of these constraints and the system’s adaptability and expressiveness, resulting in a potential tool to create narrative structures combining level design aspects with narrative.

Games are complex, multi-faceted systems that share common elements and underlying narratives, such as the conflict between a hero and a big bad enemy or pursuing a goal that requires overcoming challenges. However, identifying and describing these elements together is non-trivial as they might differ in certain properties and how players might encounter the narratives. Likewise, generating narratives also pose difficulties when encoding, interpreting, and evaluating them. To address this, we present TropeTwist, a trope-based system that can describe narrative structures in games in a more abstract and generic level, allowing the definition of games’ narrative structures and their generation using interconnected tropes, called narrative graphs. To demonstrate the system, we represent the narrative structure of three different games. We use MAP-Elites to generate and evaluate novel quality-diverse narrative graphs encoded as graph grammars, using these three hand-made narrative structures as targets. Both hand-made and generated narrative graphs are evaluated based on their coherence and interestingness, which are improved through evolution.

In this article, we propose the interactive constrained multidimensional archive of phenotypic elites (MAP-Elites), a quality-diversity solution for game content generation, implemented as a new feature of the evolutionary dungeon designer (EDD): 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 article presents the algorithm followed by an in-depth evaluation of the expressive range of all possible dimension combinations in several scenarios and discusses their influence in the fitness landscape and in the overall performance of the procedural content generation in the EDD.

We propose modeling designer style in mixed-initiative game content creation tools as archetypical design traces. These design traces are formulated as transitions between design styles; these design styles are in turn found through clustering all intermediate designs along the way to making a complete design. This method is implemented in the Evolutionary Dungeon Designer, a research platform for mixed-initiative systems to create adventure and dungeon crawler games. We present results both in the form of design styles for rooms, which can be analyzed to better understand the kind of rooms designed by users, and in the form of archetypical sequences between these rooms, i.e., designer personas.

Digitalizing tabletop games for general game playing (GGP) AI research is a continuously growing field. Tabletop Games Framework (TAG) is a framework developed to simplify the process of implementing tabletop board games to digital form. Sushi Go! is a game that combines simultaneous action selection and complete information. This creates a unique combination of mechanics, which presents a new challenge for GGP agents. By implementing Sushi Go! into TAG, we can test different agent's performance using these mechanics and compare them to their existing performances in the other games of TAG. Results of this testing are presented, which display that the framework is capable of implementing Sushi Go! and that the agents perform with mixed results. Further developing heuristics for the agents should prove to increase their performance when faced with these types of games.

Quests are a core element in many games, especially role-playing and adventure games, where quests drive the gameplay and story, engage the player in the game’s narrative, and in most cases, act as a bridge between different game elements. The automatic generation of quests and objectives is an interesting challenge since this can extend the lifetime of games such as in Skyrim, or can help create unique experiences such as in AI Dungeon. This work presents Questgram [Qg], a mixed-initiative prototype tool for creating quests using grammars combined in a mixed-initiative level design tool. We evaluated our tool quantitatively by assessing the generated quests and qualitatively through a small user study. Human designers evaluated the system by creating quests manually, automatically, and through mixed-initiative. Our results show the Questgram’s potential, which creates diverse, valid, and interesting quests using quest patterns. Likewise, it helps engage designers in the quest design process, fosters their creativity by inspiring them, and enhance the level generation facet of the Evolutionary Dungeon Designer with steps towards intertwining both level and quest design.

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.

The Evolutionary Dungeon Designer (EDD) [1] is as a mixed-initiative 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].

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.