Abstract Movement-based design methods have gained increased attention across various research fields and practices, particularly in interaction design. By engaging the body in movement, these methods have the potential to explore a richer, more intuitive, and immersive user experience. A recent project MeCaMinD1 brought together researchers from interaction and sports design to explore, collect, and document movement-based methods and make them practically useful across domains. The methods were explored through a series of workshops, furthering the team’s understanding of their use and practical applicability. This understanding was compiled into a set of design cards that we present here. We discuss the experience of using the resulting cards in movement-based design sessions. We found that while the cards are mostly used in designing, planning, and preparing sessions, they also function as support during the design sessions, used by both facilitators and participants. Based on a final ideation session with both novice and experienced facilitators, we sketch ways to support managing the cards during sessions, integrating them with movement and physical action.

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.

Multiplayer online battle arena (MOBA) games are a recent huge success both in the video game industry and the international eSports scene. These games encourage team coordination and cooperation, short and long-term planning, within a real-time combined action and strategy gameplay. Artihcial intelligence (AI) and computational intelligence (CI) in games research competitions offer a wide variety of challenges regarding the study and application of AI techniques to different game genres. These events are widely accepted by the AI/CI community as a sort of AI benchmarking that strongly influences many other research areas in the held. This paper presents and describes in detail the Dota 2 (Defense of the Ancients 2) Bot competition and the Dota 2 AI framework that supports it. This challenge aims to join both the MOBAs and AI/CI game competitions, inviting participants to submit AI controllers for the successful MOBA Dota 2 to play in 1v1 matches, which aims for fostering research on AI techniques for real-time games. The Dota 2 AI framework makes use of the actual Dota 2 game modding capabilities to enable to connect external AI controllers to actual Dota 2 game matches using the original F2P game.

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.

Mobile Virtual Reality (MVR) makes Virtual Reality games more accessible to a broader audience. Interaction design guidelines and best practices for MVR experiences are available for developers. In this paper, we specifically explore interactions in MVR games, a particular subset of MVR experiences that is becoming popular. A set of MVR games is analyzed with a special focus on head gaze, categorizing and isolating their mechanics implemented with this common MVR technique. This analysis is the basis of a test application in the MVR interactions are implemented and later compared to a traditional game pad controller in three different challenges. A comparative user study has been carried out from the perspective of both gamers and non-gamers facing these challenges. Results show the preferences and performances of the players using all the interactions, highlighting an interesting generalized preference for MVR interactions over the traditional controller in some of the analyzed cases.

Mixed-initiative Procedural Content Generation uses algorithms to assist human designers in the collaborative creation of game content. Different mixed-initiative approaches use different methods to engage with the design material while supporting the designer's intentions. However, the designer runs the risk of misunderstanding the system's abilities and how to control them. In order to limit miscommunication during the design process, heuristics could be applied.

This paper presents a grammar-guided evolutionary automatic system (GGEAS) that is capable of autonomously building special-purpose problem-solving programs. GGEAS uses a grammar-guided genetic programming (GGGP) core that generates solutions to a given problem from scratch, evolving them via selection, crossover and replacement to obtain the near-optimal solution to that problem. The GGGP core solves the closure problem and avoids code bloat. This core only outputs valid solutions and is able to freely determine their size and architecture. GGEAS is supplemented by three external modules that can be configured for any application domain: context-free grammar (CFG) generator, semantic checker and fitness module. The context-free grammar (CFG) generator creates the context-free grammar used by the GGEAS core to formalize the problem constraints. The semantic checker ensures the validity of the solutions created. Finally, the fitness module directs the population evolution towards an optimal solution to the problem. In order to test the effectiveness and the scope of the system, GGEAS has been applied to generate oscillatory biological programs codified in the BlenX language. The results show that GGEAS is effective at creating biological oscillators in silico from scratch without any prior knowledge about the solution and under a range of environmental conditions.