More-than-human approaches to HCI design research have gained traction in recent years, facilitating a need to employ new methods that decenter the human perspective. We propose live-action roleplay (larp), as a method and practice within HCI that can support perspective taking and empathy-building with non-humans actors. In this paper, we discuss a local community workshop where we facilitated numerous body-based and larp activities surrounding the theme of sustainability, and reflect on the strengths larp offers in service of decentering the human perspective.

Warm-ups, as preliminary activities to physical training, are an inherent part of any physical exercise and sports practice. Rooted in the concept of embodied cognition, embodied design is a paradigm that shifts the focus of interaction design from external artefacts and devices to the human body (Svanæs & Barkhuus, 2020). A body-centred design emphasises the importance of having the human body at the centre of the entire design cycle, conveying movement, physical expressivity, feelings, and aesthetics, in a design process that uses the body as both a resource and target. Designing with the body is, therefore, a physically demanding activity that requires a specific set of warm-ups to educate and prepare designers mentally, socially, and physically for the act of embodied design. We propose social videogames as a resourceful framework for creating such a set of warm-up exercises. This paper presents a methodology comprising off-the-shelf commercial videogames whose rules have been adapted for embodied design. Three studies have been executed to validate the game's capacity as warm-up activity and icebreaker for embodied creativity and to study the conditions for an optimal method facilitation to external instructors in preparation for an applied session. The method applied is qualitative and quantitative feedback data gathered from the three studies using questionnaires, tests, observation, and open interviews. The results of the series of studies showed the potential of the proposed methodology as warm-ups for teaching, training, and practising embodied design, as well as giving insights on how to facilitate it. Overall, the game-based warm-ups for embodied design preparation using off-the-shelf movement games have a social and playful nature. The proposed twisted gameplays make them suitable to exert body moves and get ready to think and design with their bodies. The study on facilitation shows the need for a preparation session supported by an experienced person. However, one introductory session is enough for the toolbox to become an easily configurable resource that adapts to the facilitator's needs and goals. We suggest including sample implementation cases along with instruction cards of the embodied games. Further, the warm-up games are customisable using the toolbox's modifier cards.

In this paper, we highlight how including technology, movement or play can boost a design process but with unbalanced amounts can also hamper the process. We provide a set of examples where we miscalculated the amount of technology, movement, or play that was needed in a design activity in such a way that it became counterproductive and for each example mention possible adaptations. Finally, we highlight three existing approaches that can balance the overabundance of technology, movement, and play in design processes: activity-centered design, somaesthetic design, and perspective-changing movement-based design.

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.

MnemoRoom4U is an AR (Augmented Reality) tool that uses a memory-palace strategy for foreign-language training. A memory palace helps information recall with the aid of object association in visualizations of familiar spatial surroundings. In MnemoRoom4U, paper or digital flashcards are re-placed with virtual notes containing L1 words and their L2 translations that are placed on top of real physical objects inside a familiar environment, such as one’s room, home, office space, etc. The AR-supported notes aid associative memory by establishing a relationship between the physical objects in the user’s mind and the virtual lexis to be retained in L2. Learners first set up a path through their familiar environment, attaching virtual sticky notes—each containing a target word to be memorized together with its corresponding source-language translation—to real-life objects (e.g. furniture in their homes or offices). They then take the same path again, reviewing all the words, and finally carry out a retention test. MnemoRoom4U is a technological artifact designed for specific didactic purposes in the Unity game engine with the ARCore augmented-reality plug-in for Android. This work takes a Design-Science approach with phenomenological, exploratory underpinnings tracking back to the efficiency of spatial mnemonics previously reported quantitatively and combines it with AR technology to effect L2 vocabulary recall.

In Mixed-Initiative Co-Creative tools, the human is mostly in control of what will and can be created, delegating the AI to a more suggestive role instead of a colleague in the co-creative process. Allowing more control and agency for the AI might be an interesting path in co-creative scenarios where AI could direct and take more initiative within the co-creative task. However, the relationship between AI and human designers in creative processes is delicate, as adjusting the initiative or agency of the AI can negatively affect the user experience. In this paper, different degrees of agency for the AI are explored within the Evolutionary Dungeon Designer (EDD) to further understand MI-CC tools. A user study was performed using EDD with three varying degrees of AI agency. The study highlighted elements of frustration that the human designer experiences when using the tool and the behavior in the AI that led to possible strains on the relationship. The paper concludes with the identified issues and possible solutions and suggested further research.

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.

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.

Augmented reality (AR) games are a rich environment for researching and testing computational systems that provide subtle user guidance and training. In particular computer systems that aim to augment a user's situation awareness benefit from the range of sensors and computing power available in AR headsets. The main focus of this work-in-progress paper is the introduction of the concept of the individualized Situation Awareness-based Attention Guidance (SAAG) system used to increase humans' situating awareness and the augmented reality version of the board game Carcassonne for validation and evaluation of SAAG. Furthermore, we present our initial work in developing the SAAG pipeline, the generation of game state encodings, the development and training of a game AI, and the design of situation modeling and eye-tracking processes.

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.

MAP-Elites has been successfully applied to the generation of game content and robot behaviors. However, its behavior and performance when interacted with in co-creative systems is underexplored. This paper analyzes the implications of synthetic interaction for the stability and adaptability of MAP-Elites in such scenarios. We use pre-recorded human-made level design sessions with the Interactive Constrained MAP-Elites (IC MAP-Elites). To analyze the effect of each edition step in the search space over time using different feature dimensions, we introduce Temporal Expressive Range Analysis (TERA). With TERAs, MAP-Elites is assessed in terms of its adaptability and stability to generate diverse and high-performing individuals. Our results show that interactivity, in the form of design edits and MAP-Elites adapting towards them, directs the search process to previously unexplored areas of the fitness landscape and points towards how this could improve and enrich the co-creative process with quality-diverse individuals.

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.

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.

This paper presents the Designer Preference Model, a data-driven 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.

With the growth of procedural content generation in game development, there is a need for a viable generative method to give context and make sense of the content within game space. We propose procedural narrative as context through objectives, as a useful means to structure content in games. In this paper, we present and describe an artifact developed as a sub-system to the Evolutionary Dungeon Designer (EDD) that procedurally generates objectives for the dungeons created with the tool. The quality of the content within rooms is used to generate objectives, and together with the distributions and design of the dungeon, main and side objectives are formed to maximize the usage of game space and create a proper context.

The InvisibleAI (InvAI'20) workshop aims to systematically discuss a growing class of interactive systems that invisibly remove some decision-making tasks away from humans to machines, based on recent advances in artificial intelligence (AI), data science, and sensor or actuation technology. While the interest in the affordances as well as the risks of hidden pervasive AI are high on the agenda in public debate, discussion on the topic is needed within the human-computer interaction (HCI) community. In particular, we want to gather insights, ideas, and models for approaching the use of barely noticeable AI decision-making in systems design from a human-centered perspective, so as to make the most out of the automated systems and algorithms that support human activity both as designers and users. Concurrently, these systems should safeguard that humans remain in charge when it counts (high stakes decisions, privacy, monitoring lack of explainability and fairness, etc.). What to automate and what not to automate is often a system designer's choice [8]. By taking the established concept of explicit interaction between a system and its user as a point of departure, and inviting authors to provide examples from their own research, we aim to stimulate dynamic discussion while keeping the workshop concrete and system design-focused. The workshop especially directs itself to participants from the interaction design, AI, and HCI communities. The targeted scientific outcome of the workshop is an up-to-date ontology of invisible AI-HCI systems and hybrid human-AI collaboration mechanisms, and approaches. Additionally, we expect that the workgroups and the roundtables will provide starting points shaping continued discussions, new collaborations, and innovative scientific contributions that springboard from the workgroups' findings. The focus of the proposed workshop involves the bridging of two spaces of computational research that impact user experiences and societal domains (HCI and AI). Thus, the proposed workshop topic aligns well with the theme of this year's NordiCHI conference which is Shaping Experiences, Shaping Society.

As tabletop games are ported to digital versions to increase their accessibility, the expected User Experience (UX) might be degraded in the transition. This paper aims to understand how and why playing tabletop games differentiates depending on the platform. Seven tabletop games have been chosen from different genres with an official digital adaptation. Our approach has been to do a comparative analysis of both versions followed by a user study to analyze and measure the UX differences, measuring five key factors, Usability, Engagement, Social Connectivity, Aesthetics, and Enjoyment. Our results indicate that games that rely on imperfect information offer a much higher social connectivity and engagement when played around a table. Meanwhile, games relying on tile-placement offers higher usability and engagement when played digitally due to the assistance provided by the game. However, the physical versions got, in general, a higher rating than the digital versions in all key factors except slightly in the usability. Physical versions are the preferred options, but the digital versions' benefits, such as accessibility and in-game assistance, makes them relevant for further analysis.

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. Artificial 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 field. 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.

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.

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.