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Daniel R. Little

dblp:176/3002 · DBLP profile ↗
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6ranked-venue papers
2as first author
2since 2021 · last 2025
0000-0003-3607-5525ORCID · verified

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

Artificial intelligence and machine learning · 6 · 2 first-author · 2 since 2021Applied, interdisciplinary, general and emerging computing · 5 · 2 first-author · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 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

TopicWeightPapersLastEvidence papers
Human-robot interaction
human-swarm interaction
0.912025
Asymmetrical Trust Modeling for Human-Robot Swarm Interactions · HRI 2025
Human-robot interaction
trust modeling
0.912025
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
YearPublicationVenuePosition
2025 Asymmetrical Trust Modeling for Human-Robot Swarm Interactions
abstract
Advances 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
HRI3
2023 Visual perception principles in constellation creation in individuals
Bridget A. Kelly, Charles Kemp, Daniel R. Little, Duane Hamacher, Simon J. Cropper
CogSci3
2017 The Role of Imagination in Exemplar Generation: The Effects of Conflict and Explanation
Tammy Dennis, Daniel R. Little
CogSci2
2017 Sequential Effects in the Garner Tasks
Deborah Lin, Daniel R. Little
CogSci2
2013 Working memory capacity and fluid abilities: The more difficult the item, the more more is better
Daniel R. Little, Stephan Lewandowsky, Stewart Craig
CogSci1
2012 A Bayesian Model of Rule Induction in Raven's Progressive Matrices
Daniel R. Little, Stephan Lewandowsky, Thomas L. Griffiths 0001
CogSci1