![]() ![]() The choices will be recorded in each character's individualized Story Journal. The choices made throughout the game will affect the appearance and services in the character's home instance as they progress through the story. Reward screen for completing a part of your personal story The profession chosen will also have a minor effect.ĭuring gameplay there will be a variety of different decisions the player can make when they resolve different situations, resulting in splitting the storyline into multiple branches. Each race has different choices for their biographical information. ![]() This might include the social status of the character, their belief, or other things. ![]() Several instances also offer direct checkpoints, both for replaying achievements and for a case of disconnecting.Īt character creation the player can choose different biographical information which will change aspects of what occurs during the story. Certain chapters that include achievements will always be available directly through a purple star icon for characters that have already finished the episode at least once, allowing you to replay only the necessary part without the whole story. While replaying episodes, you can progress through the episode as if you were playing it for the first time, following the objectives indicated by the green star map icon. Please note the Personal story can not be replayed. Incomplete stories will show a button to "Switch to this story," while completed episodes will show a button to "Replay this episode." These actions will replace your character's currently active story, and you will lose any progress you had made in the current chapter of that story. Visualizing two independent parameters simultaneously decreases performance in both dimensions.To switch between stories or to replay an episode, simply select the story or episode in the journal. Results show that single parameter visualizations tend to improve the tracking performance with respect to the visualized parameter, but not the non-visualized parameter. We measured the task performance under different visual feedback strategies. Visual feedback provided participants with information about the control parameters of the musical signal generated by the machine. The target pattern was a machine-generated musical signal comprising of variation over the same two parameters. Participants generated a musical signal by manipulating a hand-held device with two dimensions of control over two parameters, pitch and density of note events, and were given the task of following a target pattern as closely as possible. This paper presents results from a preliminary user study conducted to evaluate the effect of visual feedback on a musical tracking task. Previous studies indicated simultaneous music listening and performance is difficult for non-musicians, and that visual support for the task might be helpful. The goal of our research is to find ways of supporting and encouraging musical behavior by non-musicians in shared public performance environments. We also showed with a significant result that our non-linguistic audio generation achieved an 8% higher mean of average trust than using a state-of-the-art text-to-speech system. Through a user study we showed that our system was able to accurately portray a range of emotions to the user. Gestures were also driven by the symbolic phrases, encoding the emotion from the musical phrase to low degree-of-freedom movements. These phrases controlled a synthesis engine playing back pre-rendered audio samples generated through interpolation of phonemes and electronic instruments. Symbolic musical phrases were generated and tagged with emotional information by human musicians. In this paper we present a novel model built on music-driven emotional prosody and gestures that encourages the perception of a robotic identity, designed to avoid uncanny valley. Affective trust, built on emotional relationship and interpersonal bonds is particularly critical as it is more resilient to mistakes and increases the willingness to collaborate. As human-robot collaboration opportunities continue to expand, trust becomes ever more important for full engagement and utilization of robots. ![]()
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