Demo Data from EDBT 2026 / reviewers in the wild / expert
Stephen S. Killingsworth
dblp:68/6911
· DBLP profile ↗
3ranked-venue papers
1as first author
0since 2021 · last 2013
0000-0002-7204-7989ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 first-authorHuman-computer interaction and ubiquitous computing · 2Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author
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 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction › anthropomorphism
intentionality attribution |
0.1 | 1 | 2008 | Concepts about the capabilities of computers and robots: a test of the scope of adults' theory of mind · HRI 2008 |
Human-robot interaction › cognitive human-robot interaction
theory of mind |
0.1 | 1 | 2008 | Concepts about the capabilities of computers and robots: a test of the scope of adults' theory of mind · HRI 2008 |
Human-robot interaction
anthropomorphism |
0.0 | 1 | 2008 | Concepts about the capabilities of computers and robots: a test of the scope of adults' theory of mind · HRI 2008 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2013 | Connecting Learning Goals and Component Cognitive Skills in Digital Games
Stephen S. Killingsworth, Douglas B. Clark |
CogSci | 1 |
| 2013 | Tests of Concepts About Different Kinds of Minds: Predictions About the Behavior of Computers, Robots, and PeopleabstractThis research investigates adults' understanding of differences in the basic nature of intelligence exhibited by humans and by machines such as computers and robots. We tested these intuitions by asking participants to make predictions about the behaviors of different entities in situations where actions could be based on either goal-directed intentional thought or more mechanical nonintentional thought. Across several studies, adults made more intentional predictions about the behavior of humans than about the behavior of robots or computers. Although initial experiments demonstrated that participants made very similar predictions for computers and anthropomorphic robots, when asked to track robots' attention to objects, participants began to predict more intentional behaviors for the robot. A multiple regression demonstrated that differential behavioral predictions about mechanical and human entities were associated with ratings of goal understanding but not overall intelligence of current computers/rob... Daniel Levin 0001, Stephen S. Killingsworth, Megan M. Saylor, Stephen M. Gordon, Kazuhiko Kawamura |
Hum. Comput. Interact. | 2 |
| 2008 | Concepts about the capabilities of computers and robots: a test of the scope of adults' theory of mindabstractWe have previously demonstrated that people apply fundamentally different concepts to mechanical agents and human agents, assuming that mechanical agents engage in more location-based, and feature-based behaviors whereas humans engage in more goal-based, and category-based behavior. We also found that attributions about anthropomorphic agents such as robots are very similar to those about computers, unless subjects are asked to attend closely to specific intentional-appearing behaviors. In the present studies, we ask whether subjects initially do not attribute intentionality to robots because they believe that temporary limits in current technology preclude real intelligent behavior. In addition, we ask whether a basic categorization as an artifact affords lessened attributions of intentionality. We find that subjects assume that robots created with future technology may become more intentional, but will not be fully equivalent to humans, and that even a fully human-controlled robot will not be as intentional as a human. These results suggest that subjects strongly distinguish intelligent agents based on intentionality, and that the basic living/mechanical distinction is powerful enough, even in adults, to make it difficult for adults to assent to the possibility that mechanical things can be fully intentional. Daniel Levin 0001, Stephen S. Killingsworth, Megan M. Saylor |
HRI | 2 |