Florian 'Floyd' Mueller

Experiencing the Body as Play

Games research in HCI is continually interested in the human body. However, recent work suggests that the field has only begun to understand how to design bodily games. We propose that the games research field is advancing from playing with digital content using a keyboard, to using bodies to play with digital content, towards a future where we experience our bodies as digital play. To guide designers interested in supporting players to experience their bodies as play, we present two phenomenological perspectives on the human body (Körper and Leib) and articulate a suite of design tactics using our own and other peoples work. We hope with this paper, we are able to help designers embrace the point that we both have a body and are a body, thereby aiding the facilitation of the many benefits of engaging the human body through games and play, and ultimately contributing to a more humanized technological future.

Enhancing player engagement through game balancing in digitally augmented physical games

Game balancing can be used to compensate for differences in players skills, in particular in games where players compete against each other. It can help providing the right level of challenge and hence enhance engagement. However, there is a lack of understanding of game balancing design and how different game adjustments affect player engagement. This understanding is important for the design of balanced physical games. In this paper we report on how altering the game equipment in a digitally augmented table tennis game, such as the table size and bat-head size statically and dynamically, can affect game balancing and player engagement. We found these adjustments enhanced player engagement compared to the no-adjustment condition. The understanding of how the adjustments impacted on player engagement helped us to derive a set of balancing strategies to facilitate engaging game experiences. We hope that this understanding can contribute to improve physical activity experiences and encourage people to get engaged in physical activity. HighlightsWe study the effects of game adjustments on game balancing and player engagement.We study how digital technology can be used as a resource for game balancing.Dynamic adjustments enhance player engagement more than static ones.Game adjustments can moderate the influence of a player on the others performance.Game balancing design strategies are outlined.

You Better Eat to Survive: Exploring Cooperative Eating in Virtual Reality Games

You Better Eat to Survive is a two-player virtual reality game that involves eating real food to survive and ultimately escape from a virtual island. We sense eating actions of players by analyzing chewing sounds captured by a low-cost microphone attached to the players cheek. Our interest in using cooperative eating as a way of interacting in virtual reality is driven by the possibilities of creating a cross-modal gameplay experience that benefits social interactions. A user study with 22 players showed that eating real food improved players feeling of presence, challenged trust dependencies and made the survival aspect of the game feel more real. We use these insights to articulate three design themes that can guide designers in creating virtual reality games that incorporate cooperative eating. Ultimately, our work aims to guide design thinking towards using underexplored interaction methods in virtual reality games, thereby reiterating the post-digital design theme of TEI 2018.

Monte Carlo tree search based algorithms for dynamic difficulty adjustment

Maintaining player immersion is a crucial step in making an enjoyable video game. One aspect of player immersion is the level of challenge the game presents to the player. To avoid a mismatch between a players skill and the challenge of a game, which can result from traditional manual difficulty selection mechanisms (e.g. easy, medium, hard), Dynamic Difxadficulty Adjustment (DDA) has previously been proposed as a means of automatically detecting a players skill and adjusting the level of challenge the game presents accordingly. This work contributes to the field of DDA by proposing a novel approach to artificially intelligent agents for opponent control. Specifically, we propose four new DDA Artificially Intelligent (AI) agents: Reactive Outcome Sensitive Action Selection (Reactive OSAS), Proactive OSAS, and their True variants. These agents provide the player with an level of difficulty tailored to their skill in real-time by altering the action selection policy and the heuristic playout evaluation of Monte Carlo Tree Search. The DDA AI agents are tested within the FightingICE engine, which has been used in the past as an environment for AI agent competitions. The results of the experiments against other AI agents and human players show that these novel DDA AI agents can adjust the level of difficulty in real-time, by targeting a zero health difference as the outcome of the fighting game. This work also demonstrates the trade-off existing between targeting the outcome exactly (Reactive OSAS) and introducing proactive behaviour (i.e., the DDA AI agent fights even if the health difference is zero) to increase the agents believability (Proactive OSAS).

Using HTC Vive and TouchDesigner to Projection-Map Moving Objects in 3D Space: A Playful Participatory Artwork

We present the first public exhibition that uses HTC Vive and TouchDesigner to projection-map moving objects in real-time in 3D space. Our case study is an exhibition of the media artwork The Playground. During this exhibition we involved the public. They found small sculptural pieces that were hidden in public areas, they brought these pieces to an exhibition space and connected them onto a central sculpture. People could also take a photo of their city environment and upload it to a webpage. When a person entered the exhibition space our system projected this persons photo onto the small sculptural piece that was in their hand. Our system tracked and projected onto this piece as it moved around an exhibition area. All of the publics photos were also projection-mapped onto a co-created central sculpture. Our contribution benefits designers, artists and researchers who want to make playful experiences that involve projection-mapping moving objects in real-time in 3D physical space using relatively inexpensive, easily accessible tools.

Measuring player skill using dynamic difficulty adjustment

Video games have a long history of use for educational and training purposes, as they provided increased motivation and learning for players. One of the limitations of using video games in this manner is, players still need to be tested outside of the game environment to test their learning outcomes. Traditionally, determining a players skill level in a competitive game, requires players to compete directly with each other. Through the application of the Adaptive Training Framework, this work presents a novel method to determine the skill level of the player after each interaction with the video game. This is done by measuring the effort of a Dynamic Difficult Adjustment agent, without the need for direct competition between players. The experiments conducted in this research show that by measuring the players Heuristic Value Average, we can obtain the same ranking of players as state-of-the-art ranking systems, without the need for direct competition.

Playing the Subject

Today identities are considered fragmented and multiple; they are ever-changing performances. However, recent discourse surrounding identity suggests the way we engage in online media can actually essentialise identities through social sorting, creating positive feedback loops and by commodifying niche communities. We illustrate our thinking by looking at examples of current online applications that are concerned with identity and investigate how artists play with and subvert these constructs by playing many selves and producing caricatures. We do this in order to advance a discourse of identity in an age of pervasive social media.

10 Design Themes for Creating 3D Printed Physical Representations of Physical Activity Data

Self-monitoring technologies (such as heart rate monitors and activity trackers) that sense and collect physical activity data are becoming increasingly common and readily available. These devices typically represent the captured data using numbers and graphs that primarily appear on digital screens. More recently, representing data in a physical form such as 3D printed physical artifacts is gaining currency within HCI, owing to the engagement opportunities that come with physical representations. However, there exists a limited understanding of how to design such physical representations of personal data. To contribute to this understanding, we present a set of ten design themes, developed from the analysis of two independently designed systems that construct 3D printed physical artifacts from physical activity data. Each design theme describes a unique design feature that designers could incorporate in their design to make physical representations more engaging and playful. We envisage that our work would encourage and guide designers to think about different ways of supporting physical activity experiences.

Learning Options from Demonstrations: A Pac-Man Case Study

Reinforcement learning (RL) is a machine learning paradigm behind many successes in games, robotics, and control applications. RL agents improve through trial-and-error, therefore undergoing a learning phase during which they perform suboptimally. Research effort has been put into optimizing behavior during this period, to reduce its duration and to maximize after-learning performance. We introduce a novel algorithm that extracts useful information from expert demonstrations (traces of interactions with the target environment) and uses it to improve performance. The algorithm detects unexpected decisions made by the expert and infers what goal the expert was pursuing. Goals are then used to bias decisions while learning. Our experiments in the video game Pac-Man provide statistically significant evidence that our method can improve final performance compared to a state-of-the-art approach.