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Janet Z. Wang

dblp:260/1377 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—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 · 100%

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

TopicWeightPapersLastEvidence papers
Human-robot interaction
multi-party interaction
0.412020
Not Some Random Agent: Multi-person Interaction with a Personalizing Service Robot · HRI 2020
Human-robot interaction
service robot
0.412020
Not Some Random Agent: Multi-person Interaction with a Personalizing Service Robot · HRI 2020
Human-robot interaction
robot embodiment
0.112020
Not Some Random Agent: Multi-person Interaction with a Personalizing Service Robot · HRI 2020

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

user enactments · 0.4
YearPublicationVenuePosition
2020 Not Some Random Agent: Multi-person Interaction with a Personalizing Service Robot
abstract
Service robots often perform their main functions in public settings, interacting with more than one person at a time. How these robots should handle the affairs of individual users while also behaving appropriately when others are present is an open question. One option is to design for flexible agent embodiment: letting agents take control of different robots as people move between contexts. Through structured User Enactments, we explored how agents embodied within a single robot might interact with multiple people. Participants interacted with a robot embodied by a singular service agent, agents that re-embody in different robots and devices, and agents that co-embody within the same robot. Findings reveal key insights about the promise of re-embodiment and co-embodiment as design paradigms as well as what people value during interactions with service robots that use personalization.
Samantha Reig, Michal Luria, Janet Z. Wang, Danielle J. Oltman, Elizabeth J. Carter, Aaron Steinfeld, Jodi Forlizzi, John Zimmerman
HRI3