This paper introduces the notion of affective extended reality (XR) to characterise XR systems that use biodata to enable understanding of emotions. The HCI literature contains many such systems, but they have not yet been mapped into a coherent whole. To address this, we conducted a scoping review of 82 papers that explore the nexus of biodata, emotions, and XR. We analyse the technologies used in these systems, the interaction techniques employed, and the methods used to evaluate their effectiveness. Through our analysis, we contribute a mapping of the current landscape of affective XR, revealing diversity in the goals for enabling emotion sharing. We demonstrate how HCI researchers have explored the design of the interaction flows in XR biofeedback systems, highlighting key design dimensions and challenges in understanding emotions. We discuss underused approaches for emotion sharing and highlight opportunities for future research on affective XR.

Forward search algorithms, such as Monte Carlo Tree Search (MCTS), often struggle under the computational constraints of video games and other real-time environments. We address these issues by generating domain-specific macro-actions using evolutionary optimisation. Macro-actions—action abstractions that treat predefined sequences of discrete actions as a single action—can significantly improve the performance of forward search techniques by effectively increasing their look-ahead depth for the same computational cost. Evolved macro-actions can capture important structural aspects of a domain and substantially improve performance, even under tight computational constraints. We present an analysis of an evolved macro-action approach to decision-making with MCTS in real-time video games. We evaluate the effectiveness of this strategy across a large and diverse set of domains from the Arcade Learning Environment (ALE), and assess its generality, strengths, and limitations. Our results show that evolved macro-actions considerably improve performance across all tested domains and are particularly effective in games with a medium decision horizon and predictable dynamics.

The field of Sports Human-Computer Interaction (SportsHCI) investigates interaction design to support a physically active human being. Despite growing interest and dissemination of SportsHCI literature over the past years, many publications still focus on solving specific problems in a given sport. We believe in the benefit of generating fundamental knowledge for SportsHCI more broadly to advance the field as a whole. To achieve this, we aim to identify the grand challenges in SportsHCI, which can help researchers and practitioners in developing a future research agenda. Hence, this paper presents a set of grand challenges identified in a five-day workshop with 22 experts who have previously researched, designed, and deployed SportsHCI systems. Addressing these challenges will drive transformative advancements in SportsHCI, fostering better athlete performance, athlete-coach relationships, spectator engagement, but also immersive experiences for recreational sports or exercise motivation, and ultimately, improve human well-being.

Underlying humanity’s social abilities is the brain’s capacity to interpersonally synchronize. Experimental, lab-based neuropsychological studies have demonstrated that inter-brain synchrony can be technologically mediated. However, knowledge in deploying these technologies in-the-wild and studying their user experience, an area HCI excels in, is lacking. With advances in mobile brain sensing and stimulation, we identify an opportunity for HCI to investigate the in-the-wild augmentation of inter-brain synchrony. We designed “PsiNet,” the first wearable brain-to-brain system aimed at augmenting inter-brain synchrony in-the-wild. Participant interviews illustrated three themes that describe the user experience of modulated inter-brain synchrony: hyper-awareness; relational interaction; and the dissolution of self. We contribute these three themes to assist HCI theorists’ discussions of inter-brain synchrony experiences. We also present three practical design tactics for HCI practitioners designing inter-brain synchrony, and hope that our work guides a HCI future of brain-to-brain experiences which fosters human connection.

Personalized video games have the potential to provide unique and meaningful experiences for the player. User data taken from biosensors, questionnaires, or in-game performance data can infer a player’s psychological state, to which relevant game features can be adapted to enhance the player experience. This survey discusses the data types, game elements, and methods that have been used thus far to create adaptive experiences in games. The survey specifically focuses on personalized nonplayer character (NPC) companions through adaptation. Studies using performance data, affect and cognition, and self-reports to adapt companions and other NPC types are reviewed for their success in providing an enhanced experience. We then provide a motivation for a personalized companion before detailing a framework for an adaptive system that modifies companion characteristics based on the player’s state. This framework takes a human-centered approach to personalized companion design; it proposes the game elements appropriate for adaptation, the data types that suit the adaptation of the companion type, the techniques that would enable successful adaptation, and methods for companion evaluation. The framework firstly quantifies the companion’s behaviour according to recent companion design architecture. Following this, the companion’s characteristics are updated according to the player state, previous player states, evaluations of changes, and the player model. The last phase highlights how to evaluate the companion during the design stage to ensure that it is reliable in assisting the player. We suggest this as a starting point for game designers when considering how to approach a companion that aims to enhance and sustain player experience.

Understanding the interaction between cognitive load and features of video game play is important for the accurate measurement and application of this psychological construct in both research and industry scenarios to enhance the player experience. A challenge within this domain is the use of measurements developed in different areas in video games without first validating or testing the reliability of these tools. We present a holistic evaluation of methods used to measure cognitive load during naturalistic gameplay of a commercially available sandbox game. This study measures electroencephalography, electromyography, heart rate, heart rate variability, electrodermal activity, and eye blink rate from players during a building task in Minecraft whilst being assisted by a virtual companion. Various models reveal that a number of physiological measures can be used as a proxy of subjective cognitive load measurement, providing further insight into the player experience during gameplay. Our analysis also reveals some discriminant validity of the subjective measurement used. These results can help inform the choice of sensor in evaluating video game features, or when needing to accurately measure cognitive load for an adaptive situation or high-risk scenario.

This research proposes an evaluation of pitch-based sonification methods via user experiments in real-life scenarios, specifically vertical guidance, with the aim of standardizing the use of audio interfaces in AR in guidance tasks. Using literature on assistive technology for people who are blind or visually impaired, we aim to generalize their applicability to a broader population and for different use cases. We propose and test sonification methods for vertical guidance in a series of hand-navigation assessments with users without visual feedback. Including feedback from a visually impaired expert in digital accessibility, results (N=19) outlined that methods that do not rely on memorizing pitch had the most promising accuracy and self-reported workload performances. Ultimately, we argue for audio AR's ability to enhance user performance in different scenarios, from video games to finding objects in a pantry.

To promote empathy with people that have disabilities, we propose a multi-sensory interactive experience that allows sighted users to embody having a visual impairment whilst using assistive technologies. The experiment involves blindfolded sighted participants interacting with a variety of sonification methods in order to locate targets and place objects in a real kitchen environment. Prior to the tests, we enquired about the perceived benefits of increasing said empathy from the blind and visually impaired (BVI) community. To test empathy, we adapted an Empathy and Sympathy Response scale to gather sighted people's self-reported and perceived empathy with the BVI community from both sighted (N = 77) and BVI people (N = 20) respectively. We re-tested sighted people's empathy after the experiment and found that their empathetic and sympathetic responses (N = 15) significantly increased. Furthermore, survey results suggest that the BVI community believes the use of these empathy-evoking embodied experiences may lead to the development of new assistive technologies.

We present a framework for the visualization of mechanical stress using augmented reality (AR) using Thermoelastic Stress Analysis (TSA). The 2D stress images generated by TSA are converted to a 3D stress map using computer vision technology and then superimposed on the real object using AR. Our framework enables in-situ visualization of stress in geometrically complex structural components, which can assist in the design, manufacture, test, and through-life sustainment of failure-critical engineering assets. We also discuss the challenges of such a TSA-AR combination and present a case study that demonstrates the performance and significance of our system.

Brain-computer interface (BCI) systems hold the potential to foster human flourishing and self-actualization. However, we believe contemporary BCI system design approaches unnecessarily limit these potentialities as they are approached from a traditional interaction perspective, producing command-response experiences. This article proposes to go beyond “interaction” and toward a paradigm of human-computer integration. The potential of this paradigm is demonstrated through three prototypes: Inter-Dream, a system that integrates with the brain's autonomic physiological processes to drive users toward healthy sleep states; Neo-Noumena, a system that integrates with the user's affective neurophysiology to augment the interpersonal communication of emotion; and PsiNet, a system that integrates interpersonal brain activity to amplify human connection. Studies of these prototypes demonstrate the benefits of the integration paradigm in realizing the multifaceted benefits of BCI systems, and this work presents the brain-computer integration framework to help guide designers of future BCI integrations.

The subjective experience of emotion is notoriously difficult to interpersonally communicate. We believe that technology can challenge this notion through the design of neuroresponsive systems for interpersonal communication. We explore this through "Neo-Noumena", a communicative neuroresponsive system that uses brain-computer interfacing and artificial intelligence to read one's emotional states and dynamically represent them to others in mixed reality through two head-mounted displays. In our study five participant pairs were given Neo-Noumena for three days, using the system freely. Measures of emotional competence demonstrated a statistically significant increase in participants' ability to interpersonally regulate emotions. Furthermore, participant interviews revealed themes regarding Spatiotemporal Actualization, Objective Representation, and Preternatural Transmission. We also suggest design strategies for future augmented emotion communication systems. We intend that work gives guidance towards a future in which our ability to interpersonally communicate emotion is augmented beyond traditional experience.

Theme parks visits can be very playful events for families, however, waiting in the ride’s queues can often be the cause of great frustration. We developed a novel augmented reality game to be played in the theme park’s queue, and an in-the-wild study with X participants using log data and interviews demonstrated that every minute playing was perceived to the same extent of about 5 minutes of not playing the game. We articulate a design space for researchers and strategies for game designers aiming to reduce perceived waiting time in queues. With our work, we hope to extend how we use games in everyday life to make our lives more playful.

Sparse reward games, such as the infamous Montezuma’s Revenge, pose a significant challenge for Reinforcement Learning (RL) agents. Hierarchical RL, which promotes efficient exploration via subgoals, has shown promise in these games. However, existing agents rely either on human domain knowledge or slow autonomous methods to derive suitable subgoals. In this work, we describe a new, autonomous approach for deriving subgoals from raw pixels that is more efficient than competing methods. We propose a novel intrinsic reward scheme for exploiting the derived subgoals, applying it to three Atari games with sparse rewards. Our agent’s performance is comparable to that of state-of-the-art methods, demonstrating the usefulness of the subgoals found.

Poor sleep has been acknowledged as an increasingly prevalent global health concern, however, how to design for promoting sleep is relatively underexplored. We propose neurofeedback technology may potentially facilitate restfulness and sleep onset, and we explore this through the creation and study of "Inter-Dream", a novel multisensory interactive artistic experience driven by neurofeedback. Twelve participants individually rested, augmented by Inter-Dream. Results demonstrated: statistically significant decreases in pre-sleep cognitive arousal (p = .01), negative emotion (p = .008), and negative affect (p = .004). EEG readings were also indicative of restorative restfulness and cognitive stillness, while interview responses described experiences of mindfulness and playful self-exploration. Taken together, our work highlights neurofeedback as a potential pathway for future research in the promotion of sleep, while also suggesting strategies for designing towards this within the context of pre-sleep.

Aside from hand-coded bots, most of the videogame agents rely on experience in one way or another. Some agents improve over time by adjusting to real experience, while others make projections by leveraging simulated experience. In one sense, human play seems to resemble simulation, in that human players often try to visualize possible trajectories before deciding on a real course of action. However, most of the existing simulation-based agents require precise knowledge of the game's code, and their search depth is limited by the granular time scales encountered in videogames. Human players, on the other hand, appear to visualize plans in terms of abstract “skills,” such as running and jumping. These skills are learned from real experience, so,in this sense, human play resembles learning-based approaches. Motivated by these observations, we propose an approach that bridges the gap between skills, which are uncertain in outcome and duration, and traditional simulation-based planning, which is discrete. We apply this approach to maze-like navigation problems in Infinite Mario. After an initial skill acquisition phase, our agent is capable of navigating new levels without further training and also scales better with goal distance than a granular simulation method that exploits exact knowledge of the game's physics.

Applications of reinforcement learning to continuous control tasks often rely on a steady, informative reward signal. In videogames, however, tasks may be far easier to specify through a binary reward that indicates success or failure. In the absence of a steady, guiding reward, the agent may struggle to explore efficiently, especially if effective exploration requires strong coordination between actions. In this paper, we show empirically that this issue may be mitigated by exploring over an abstract action set, using hierarchically composed parameterized skills. We experiment in two tasks with sparse rewards in a continuous control environment based on the arcade game Asteroids. Compared to a flat learner that explores symmetrically over low-level actions, our agent explores a greater variety of useful actions, and its long-term performance on both tasks is superior.

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.

In platform videogames, players are frequently tasked with solving medium-term navigation problems in order to gather items or powerups. Artificial agents must generally obtain some form of direct experience before they can solve such tasks. Experience is gained either through training runs, or by exploiting knowledge of the game's physics to generate detailed simulations. Human players, on the other hand, seem to look ahead in high-level, abstract steps. Motivated by human play, we introduce an approach that leverages not only abstract "skills", but also knowledge of what those skills can and cannot achieve. We apply this approach to Infinite Mario, where despite facing randomly generated, maze-like levels, our agent is capable of deriving complex plans in real-time, without relying on perfect knowledge of the game's physics.

Player Modelling is one of the challenges in Interactive Narratives (INs), where a precise representation of the players mental state is needed to provide a personalised experience. However, how to represent the interaction of the player with the game to make the appropriate decision in the story is still an open question. In this paper, we aim to bridge this gap identifying the information needed to capture the players interaction with an IN using the Belief-Desire-Intention (BDI) model of agency. We present a BDI design to mimic a players interaction with a simplified version of the interactive fiction Anchorhead.

Q-learning associates states and actions of a Markov Decision Process to expected future reward through online learning. In practice, however, when the state space is large and experience is still limited, the algorithm will not find a match between current state and experience unless some details describing states are ignored. On the other hand, reducing state information affects long term performance because decisions will need to be made on less informative inputs. We propose a variation of Q-learning that gradually enriches state descriptions, after enough experience is accumulated. This is coupled with an ad-hoc exploration strategy that aims at collecting key information that allows the algorithm to enrich state descriptions earlier. Experimental results obtained by applying our algorithm to the arcade game Pac-Man show that our approach significantly outperforms Q-learning during the learning process while not penalizing long-term performance.

In this paper, we propose the strategy of integrating multiple evolutionary processes for personalized procedural content generation (PCG). In this vein, we provide a concrete solution that personalizes game maps in a top-down action-shooter game to suit an individual player's preferences. The need for personalized PCG is steadily growing as the player market diversifies, making it more difficult to design a game that will accommodate a broad range of preferences and skills. In the solution presented here, the geometry of the map and the density of content within that geometry are represented and generated in distinct evolutionary processes, with the player's preferences being captured and utilized through a combination of interactive evolution and a player model formulated as a recommender system. All these components were implemented into a test bed game and experimented on through an unsupervised public experiment. The solution is examined against a plausible random baseline that is comparable to random map generators that have been implemented by independent game developers. Results indicate that the system as a whole is receiving better ratings, that the geometry and content evolutionary processes are exploring more of the solution space, and that the mean prediction accuracy of the player preference models is equivalent to that of existing recommender system literature. Furthermore, we discuss how each of the individual solutions can be used with other game genres and content types.

Imitating human-like behaviour in action games is a challenging but intriguing task in Artificial Intelligence research, with various strategies being employed to solve the human-like imitation problem. In this research we consider learning human-like behaviour via Markov decision processes without being explicitly given a reward function, and learning to perform the task by observing expert's demonstration. Individual players often have characteristic styles when playing the game, and this method attempts to find the behaviours which make them unique. During play sessions of Super Mario we calculate player's behaviour policies and reward functions by applying inverse reinforcement learning to the player's actions in game. We conduct an online questionnaire which displays two video clips, where one is played by a human expert and the other is played by the designed controller based on the player's policy. We demonstrate that by using apprenticeship learning via Inverse Reinforcement Learning, we are able to get an optimal policy which yields performance close to that of an human expert playing the game, at least under specific conditions.

This paper demonstrates an approach to arranging content within maps of an action-shooter game. Content here refers to any virtual entity that a player will interact with during game-play, including enemies and pick-ups. The content layout for a map is indirectly represented by a Compositional Pattern-Producing Networks (CPPN), which are evolved through the Neuroevolution of Augmenting Topologies (NEAT) algorithm. This representation is utilized within a complete procedural map generation system in the game PCG: Angry Bots. In this game, after a player has experienced a map, a recommender system is used to capture their feedback and construct a player model to evaluate future generations of CPPNs. The result is a content layout scheme that is optimized to the preferences and skill of an individual player. We provide a series of case studies that demonstrate the system as it is being used by various types of players.

Abstract Real cloth exhibits bending effects, such as residual curvatures and permanent wrinkles. These are typically explained by bending plastic deformation due to internal friction in the fibre and yarn structure. Internal friction also gives rise to energy dissipation which significantly affects cloth dynamic behaviour. In textile research, hysteresis is used to analyse these effects, and can be modelled using complex friction terms at the fabric geometric structure level. The hysteresis loop is central to the modelling and understanding of elastic and inelastic (plastic) behaviour, and is often measured as a physical characteristic to analyse and predict fabric behaviour. However, in cloth simulation in computer graphics the use of hysteresis to capture these effects has not been reported so far. Existing approaches have typically used plasticity models for simulating plastic deformation. In this paper, we report on our investigation into experiments using a simple mathematical approximation to an ideal hysteresis loop at a high level to capture the previously mentioned effects. Fatigue weakening effects during repeated flexural deformation are also considered based on the hysteresis model. Comparisons with previous bending models and plasticity methods are provided to point out differences and advantages. The method requires only incremental extra computation time.

This paper provides a review of existing approaches to using evolutionary algorithms (EA) during procedural terrain generation (PTG) processes in video games. A reliable PTG algorithm would allow game maps to be created partially or completely autonomously, reducing the development cost of a game and providing players with more content. Specifically, the use of EA raises possibilities of more control over the terrain generation process, as well as the ability to tailor maps for individual users. In this paper we outline the prominent algorithms that use EA in terrain generation, describing their individual advantages and disadvantages. This is followed by a comparison of the core features of these approaches and an analysis of their appropriateness for generating game terrain. This survey concludes with open challenges for future research.

Procedurally generating content for video games is gaining interest as an approach to mitigate rising development costs and meet users' expectations for a broader range of experiences. This paper explores the use of evolutionary algorithms to aid in the content generation process, especially the creation of three-dimensional terrain. We outline a prototype for the generation of in-game terrain by compiling smaller height-map patches that have been extracted from sample maps. Evolutionary algorithms are applied to this generation process by using crossover and mutation to evolve the layout of the patches. This paper demonstrates the benefits of an interactive two-level parent selection mechanism as well as how to seamlessly stitch patches of terrain together. This unique patch-based terrain model enhances control over the evolution process, allowing for terrain to be refined more intuitively to meet the user's expectations.

Abstract In this paper, an investigation is conducted on the security issues in massive multiplayer games. A taxonomy framework for online cheating is provided. Under this proposed framework, online cheating is classified and state of the art counter‐cheating techniques are analysed with emphasis on attacks that pose a considerable challenge to the security of massive multiplayer online games. Copyright © 2008 John Wiley & Sons, Ltd.