5 research outputs found
MIRIAM: A Multimodal Chat-Based Interface for Autonomous Systems
We present MIRIAM (Multimodal Intelligent inteRactIon for Autonomous
systeMs), a multimodal interface to support situation awareness of autonomous
vehicles through chat-based interaction. The user is able to chat about the
vehicle's plan, objectives, previous activities and mission progress. The
system is mixed initiative in that it pro-actively sends messages about key
events, such as fault warnings. We will demonstrate MIRIAM using SeeByte's
SeeTrack command and control interface and Neptune autonomy simulator.Comment: 2 pages, ICMI'17, 19th ACM International Conference on Multimodal
Interaction, November 13-17 2017, Glasgow, U
Explain Yourself: A Natural Language Interface for Scrutable Autonomous Robots
Autonomous systems in remote locations have a high degree of autonomy and
there is a need to explain what they are doing and why in order to increase
transparency and maintain trust. Here, we describe a natural language chat
interface that enables vehicle behaviour to be queried by the user. We obtain
an interpretable model of autonomy through having an expert 'speak out-loud'
and provide explanations during a mission. This approach is agnostic to the
type of autonomy model and as expert and operator are from the same user-group,
we predict that these explanations will align well with the operator's mental
model, increase transparency and assist with operator training.Comment: 2 pages. Peer reviewed position paper accepted in the Explainable
Robotic Systems Workshop, ACM Human-Robot Interaction conference, March 2018,
Chicago, IL US
Assessing the relationship between subjective trust, confidence measurements, and mouse trajectory characteristics in an online task
Trust is essential for our interactions with others but also with artificial
intelligence (AI) based systems. To understand whether a user trusts an AI,
researchers need reliable measurement tools. However, currently discussed
markers mostly rely on expensive and invasive sensors, like
electroencephalograms, which may cause discomfort. The analysis of mouse
trajectory has been suggested as a convenient tool for trust assessment.
However, the relationship between trust, confidence and mouse trajectory is not
yet fully understood. To provide more insights into this relationship, we asked
participants (n = 146) to rate whether several tweets were offensive while an
AI suggested its assessment. Our results reveal which aspects of the mouse
trajectory are affected by the users subjective trust and confidence ratings;
yet they indicate that these measures might not explain sufficiently the
variance to be used on their own. This work examines a potential low-cost trust
assessment in AI systems.Comment: Submitted to CHI 2023 and rejecte