VLDB 2026 Research / reviewers in the wild / expert
Daniel A. Williams
dblp:317/5511
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
0000-0002-2983-7707ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 1 since 2021
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 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction
human-swarm interaction |
0.9 | 1 | 2025 | Asymmetrical Trust Modeling for Human-Robot Swarm Interactions · HRI 2025 |
Human-robot interaction
trust modeling |
0.9 | 1 | 2025 | Asymmetrical Trust Modeling for Human-Robot Swarm Interactions · HRI 2025 |
Methods — techniques the papers use, named apart from their topics
user study · 0.9switched linear system · 0.9model-based observer · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Asymmetrical Trust Modeling for Human-Robot Swarm InteractionsabstractAdvances in the control of autonomous systems have accompanied an expansion in the potential applications for autonomous robotic swarms. The success of applications involving humans depends on the quality of interaction with the swarm, particularly the trust that the commander places in the swarm. Absent from the literature is the design of commander trust dynamics that incorporate asymmetric responses to swarm performance. This paper focuses on developing an estimated trust model that employs a switched linear system structure. The identified model is used in a model-based observer that eliminates the need for self-reported trust measurements. Results from a recent user study with 51 participants illustrate considerations during the estimation of population parameters for such nonlinear model-based observers. It is anticipated that such a trust observer can be used to augment communication interfaces for human-swarm interactions in complex environments, leading to better performing systems incorporating humans in the loop. Daniel A. Williams, Airlie Chapman, Daniel R. Little, Chris Manzie |
HRI | 1 |