Towards player-driven procedural content generation

Generating immersive game content is one of the ultimate goals for a game designer. This goal can be achieved by realizing the fact that players' perception of the same game differ according to a number of factors including: players' personality, playing styles, expertise and culture background. While one player might find the game immersive, others may quit playing as a result of encountering a seemingly insoluble problem. One promising avenue towards optimizing the gameplay experience for individual game players is to tailor player experience in real-time via automatic game content generation. Specifying the aspects of the game that have the major influence on the gameplay experience, identifying the relationship between these aspect and each individual experience and defining a mechanism for tailoring the game content according to each individual needs are important steps towards player-driven content generation.peer-reviewe

Exploiting the robot kinematic redundancy for emotion conveyance to humans as a lower priority task

Current approaches do not allow robots to execute a task and simultaneously convey emotions to users using their body motions. This paper explores the capabilities of the Jacobian null space of a humanoid robot to convey emotions. A task priority formulation has been implemented in a Pepper robot which allows the specification of a primary task (waving gesture, transportation of an object, etc.) and exploits the kinematic redundancy of the robot to convey emotions to humans as a lower priority task. The emotions, defined by Mehrabian as points in the pleasure–arousal–dominance space, generate intermediate motion features (jerkiness, activity and gaze) that carry the emotional information. A map from this features to the joints of the robot is presented. A user study has been conducted in which emotional motions have been shown to 30 participants. The results show that happiness and sadness are very well conveyed to the user, calm is moderately well conveyed, and fear is not well conveyed. An analysis on the dependencies between the motion features and the emotions perceived by the participants shows that activity correlates positively with arousal, jerkiness is not perceived by the user, and gaze conveys dominance when activity is low. The results indicate a strong influence of the most energetic motions of the emotional task and point out new directions for further research. Overall, the results show that the null space approach can be regarded as a promising mean to convey emotions as a lower priority task.Postprint (author's final draft

Artificial Companions with Personality and Social Role

Subtitle: "Expectations from Users on the Design of Groups of Companions"International audienceRobots and virtual characters are becoming increasingly used in our everyday life. Yet, they are still far from being able to maintain long-term social relationships with users. It also remains unclear what future users will expect from these so-called "artificial companions" in terms of social roles and personality. These questions are of importance because users will be surrounded with multiple artificial companions. These issues of social roles and personality among a group of companions are sledom tackled in user studies. In this paper, we describe a study in which 94 participants reported that social roles and personalities they would expect from groups of companions. We explain how the resulsts give insights for the design of future groups of companions endowed with social intelligence

Using Video Activity Reports to Support Remote Project-Based Learning

Distance learning has been expanding. Learner engagement is particularly important in project-based learning (PBL), but the interaction between teacher and learner and the understanding of learner status, including engagement, is not easy. This study aims to support teacher-learner communication based on learner engagement for remote PBL. In this paper, we propose the use of video activity reports by learners to estimate and understand learner engagement and to demonstrate its feasibility on the basis of the relationship between verbal and nonverbal information that can be obtained from video activity reports and learner engagement. Analysis of 232 video activity reports submitted by eight graduate students while working on remote research-based PBLs reveals that learner engagement decreases (1) when the report contained negative words, (2) when filled pauses were frequent or long, and (3) when silent pauses were infrequent or short. Furthermore, the feasibility of an AI-based support system is demonstrated through the design and implementation of a prototype

Mediated participatory design for contextually aware in-vehicle user-experiences with autonomous vehicles

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThis study reports on the empirical findings of a series of participatory design workshops for the development of a supportive automotive user experience design system. Identifying and addressing this area with traditional research methods is problematic due to the different user experience (UX) design perspectives that might conflict and the related limitations of the automotive domain. Consequently, we deploy a pragmatic epistemological paradigm and apply participatory prototyping methods to resolve this problem. We conduct two iterations of design and evaluation with 19 user experience (UX) designers through individual participatory prototyping activities to gain insights into their explicit, observable, tacit and latent needs. We describe the design of a toolkit tailored to the character of the study to be used in relevant studies of ill-defined or wicked problems. The participatory design activities initially allowed us to explore the motivation to use different technologies, the system’s architecture, detailed features of interactivity, and to describe our users’ needs. As a result, our first analysis of data led us to design implications that translate participants’ needs into UX goals. We use these UX goals for the design of goal-directed personas and scenarios of use as actionable insights to develop our system. A medium-fidelity functional prototype of our system was then evaluated, while contextually aware automotive UX practitioners criticised our design decisions. Some of the essential findings when supporting the contextual understanding are generating new knowledge to inform both theory and practice. The results propose that most automotive UX designers are ready to adopt technologies that use sensitive physiological measures such as eyes, face, body tracking using cameras and computer vision. In contrast, non-automotive UX designers who empathise with the passengers and the drivers and perceive the in-vehicle space as something more private are suggesting that this might affect people’s trust. The majority agrees to collect data and communicate with the users using implicit and explicit context, as a way to support UX design in the autonomous vehicles would require the consent of the passengers. Even though UX designers suggested a general interest in the social and temporal context of the interactions, the limitations of privacy and safety in the vehicle limit them in collecting task-related contextual data leaving the social, temporal, and physical context unexplored. Safety is arguably a factor that will not restrict the future of autonomous driving experiences research and design since there is no cognitive demand on level five autonomy which hands the passengers with plenty of other options when not driving, assuming that they are ready to trust a fully automated system. However, our study does not provide us with a direction on the privacy of autonomous vehicle experiences and whether privacy will continue being a limitation in the context of self-driving vehicles. Thus, we would recommend further research on trust and privacy in fully automated vehicles. We conclude by discussing the design implications and functional tools of our system, including 1) a video tagging tool that supports saving an occurrence identified momentarily on real-time video. 2) A privacy call-wall which uses implicit and explicit context to avoid intrusiveness in private situations. 3) A human-like avatar tool for mitigating privacy issues, and 4) an interactive interviewing tool to support communication between UXers and the passengers of autonomous vehicles. Finally, 5) exploration tools, including a tool for searching participants’ characteristics and target groups of people. We further inform the body of knowledge in participatory UX and HCI methods about the advantages of our methodological approach and the limitations of using it. We discuss why involving non-experts in co-design activities using toolkits tailored to the domain of interest is valuable. Furthermore, we extensively address how, and we give directions for the design of similar toolkits by describing the toolkit that we designed and applied in our study. Conclusively we discuss the broader implications of trust and privacy in other domains and how this related to our findings

The Impact of Robot Tutor Social Behaviour on Children

Robotic technologies possess great potential to enter our daily lives because they have the ability to interact with our world. But our world is inherently social. Whilst humans often have a natural understanding of this complex environment, it is much more challenging for robots. The field of social Human-Robot Interaction (HRI) seeks to endow robots with the characteristics and behaviours that would allow for intuitive multimodal interaction. Education is a social process and previous research has found strong links between the social behaviour of teachers and student learning. This therefore presents a promising application opportunity for social human-robot interaction. The thesis presented here is that a robot with tailored social behaviour will positively influence the outcomes of tutoring interactions with children and consequently lead to an increase in child learning when compared to a robot without this social behaviour. It has long been established that one-to-one tutoring provides a more effective means of learning than the current typical school classroom model (one teacher to many students). Schools increasingly supplement their teaching with technology such as tablets and laptops to offer this personalised experience, but a growing body of evidence suggests that robots lead to greater learning than other media. It is posited that this is due to the increased social presence of a robot. This work adds to the evidence that robots hold a social advantage over other technological media, and that this indeed leads to increased learning. In addition, the work here contributes to existing knowledge by seeking to expand our understanding of how to manipulate robot social behaviour in educational interactions such that the behaviour is tailored for this purpose. To achieve this, a means of characterising social behaviour is required, as is a means of measuring the success of the behaviour for the interaction. To characterise the social behaviour of the robot, the concept of immediacy is taken from the human-human literature and validated for use in HRI. Greater use of immediacy behaviours is also tied to increased cognitive learning gains in humans. This can be used to predict the same effect for the use of social behaviour by a robot, with learning providing an objective measure of success for the robot behaviour given the education application. It is found here that when implemented on a robot in tutoring scenarios, greater use of immediacy behaviours generally does tend to lead to increased learning, but a complex picture emerges. Merely the addition of more social behaviour is insufficient to increase learning; it is found that a balance should be struck between the addition of social cues, and the congruency of these cues