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Julia Novakoff

dblp:215/6606 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 62% Human-AI interaction · 19% Collaborative and social computing · 19%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction › robot communication
robot dialogue
0.312018
"Thank You for Sharing that Interesting Fact!": Effects of Capability and Context on Indirect Speech Act Use in Task-Based Human-Robot Dialogue · HRI 2018
Collaborative and social computing › online communities
social norms
0.112018
"Thank You for Sharing that Interesting Fact!": Effects of Capability and Context on Indirect Speech Act Use in Task-Based Human-Robot Dialogue · HRI 2018
Human-AI interaction › conversational interaction
task-oriented dialogue
0.112018
"Thank You for Sharing that Interesting Fact!": Effects of Capability and Context on Indirect Speech Act Use in Task-Based Human-Robot Dialogue · HRI 2018

Methods — techniques the papers use, named apart from their topics

wizard-of-oz · 0.3
YearPublicationVenuePosition
2018 "Thank You for Sharing that Interesting Fact!": Effects of Capability and Context on Indirect Speech Act Use in Task-Based Human-Robot Dialogue
abstract
Naturally interacting robots must be able to understand natural human speech. As such, recent work has sought to allow robots to infer the intentions behind commonly used non-literal utterances such as indirect speech acts (ISAs). However, it is still unclear to what extent ISAs will actually be used in task-based human-robot dialogue, and to what extent robots could function without the ability to understand ISAs. In this paper, we present the results of a Wizard-of-Oz experiment that examined human ISA use in scenarios that did or did not have conventionalized social norms, and analyzed both ISA use and perceptions of robots when robots were or were not capable of understanding ISAs. Our results suggest that (1) ISAs are commonly used in task-based human-robot dialogues, even when robots show themselves unable to understand ISAs; (2) ISA use is more common in contexts with conventionalized social norms; and (3) a robot's inability to understand ISAs harms both the robot's task performance and human perception of the robot.
Tom Williams 0001, Daria Thames, Julia Novakoff, Matthias Scheutz
HRI3