7,803 research outputs found
A Virtual Conversational Agent for Teens with Autism: Experimental Results and Design Lessons
We present the design of an online social skills development interface for
teenagers with autism spectrum disorder (ASD). The interface is intended to
enable private conversation practice anywhere, anytime using a web-browser.
Users converse informally with a virtual agent, receiving feedback on nonverbal
cues in real-time, and summary feedback. The prototype was developed in
consultation with an expert UX designer, two psychologists, and a pediatrician.
Using the data from 47 individuals, feedback and dialogue generation were
automated using a hidden Markov model and a schema-driven dialogue manager
capable of handling multi-topic conversations. We conducted a study with nine
high-functioning ASD teenagers. Through a thematic analysis of post-experiment
interviews, identified several key design considerations, notably: 1) Users
should be fully briefed at the outset about the purpose and limitations of the
system, to avoid unrealistic expectations. 2) An interface should incorporate
positive acknowledgment of behavior change. 3) Realistic appearance of a
virtual agent and responsiveness are important in engaging users. 4)
Conversation personalization, for instance in prompting laconic users for more
input and reciprocal questions, would help the teenagers engage for longer
terms and increase the system's utility
The Operation of Autonomous Mobile Robot Assistants in the Environment of Care Facilities Adopting a User-Centered Development Design
The successful development of autonomous mobile robot assistants depends significantly on the well-balanced reconcilements of the technically possible and the socially desirable. Based on empirical research 2 substantiated conclusions can be established for the suitability of "scenario-based design" (Rosson/Carroll 2003) for the successful development of mobile robot assistants and automated guided vehicles to be applied for service functions in stationary care facilities for seniors.User-Centered Technology Development, Knowledge-Transfer, Participative Assessment Methods, Robotics
Interactive Execution Monitoring of Agent Teams
There is an increasing need for automated support for humans monitoring the
activity of distributed teams of cooperating agents, both human and machine. We
characterize the domain-independent challenges posed by this problem, and
describe how properties of domains influence the challenges and their
solutions. We will concentrate on dynamic, data-rich domains where humans are
ultimately responsible for team behavior. Thus, the automated aid should
interactively support effective and timely decision making by the human. We
present a domain-independent categorization of the types of alerts a plan-based
monitoring system might issue to a user, where each type generally requires
different monitoring techniques. We describe a monitoring framework for
integrating many domain-specific and task-specific monitoring techniques and
then using the concept of value of an alert to avoid operator overload. We use
this framework to describe an execution monitoring approach we have used to
implement Execution Assistants (EAs) in two different dynamic, data-rich,
real-world domains to assist a human in monitoring team behavior. One domain
(Army small unit operations) has hundreds of mobile, geographically distributed
agents, a combination of humans, robots, and vehicles. The other domain (teams
of unmanned ground and air vehicles) has a handful of cooperating robots. Both
domains involve unpredictable adversaries in the vicinity. Our approach
customizes monitoring behavior for each specific task, plan, and situation, as
well as for user preferences. Our EAs alert the human controller when reported
events threaten plan execution or physically threaten team members. Alerts were
generated in a timely manner without inundating the user with too many alerts
(less than 10 percent of alerts are unwanted, as judged by domain experts)
Investigating the Effect of Trust Manipulations on Affect over Time in Human-Human versus Human-Robot Interactions
The current study explored the influence of trust and distrust behaviors on affect over time. We examined the differences in affect when participants (N=97) were paired with a human or a robot while playing amodified version of the investorgame. Results indicated that there were no differences in affect between partner types when the partner performed a trustful behavior. When the partner performed a distrustful behavior, positive affect was higher for human partners than for robot partners. When robot partners performed a distrustful behavior, negative affect had a steeper incline compared to human partners. These findings suggest that people are more sensitive to distrust behaviors that are performed by a robot over a human
Intelligence for Human-Assistant Planetary Surface Robots
The central premise in developing effective human-assistant planetary surface robots is that robotic intelligence is needed. The exact type, method, forms and/or quantity of intelligence is an open issue being explored on the ERA project, as well as others. In addition to field testing, theoretical research into this area can help provide answers on how to design future planetary robots. Many fundamental intelligence issues are discussed by Murphy [2], including (a) learning, (b) planning, (c) reasoning, (d) problem solving, (e) knowledge representation, and (f) computer vision (stereo tracking, gestures). The new "social interaction/emotional" form of intelligence that some consider critical to Human Robot Interaction (HRI) can also be addressed by human assistant planetary surface robots, as human operators feel more comfortable working with a robot when the robot is verbally (or even physically) interacting with them. Arkin [3] and Murphy are both proponents of the hybrid deliberative-reasoning/reactive-execution architecture as the best general architecture for fully realizing robot potential, and the robots discussed herein implement a design continuously progressing toward this hybrid philosophy. The remainder of this chapter will describe the challenges associated with robotic assistance to astronauts, our general research approach, the intelligence incorporated into our robots, and the results and lessons learned from over six years of testing human-assistant mobile robots in field settings relevant to planetary exploration. The chapter concludes with some key considerations for future work in this area
Human-Machine Communication: Complete Volume. Volume 6
his is the complete volume of HMC Volume 6
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