“Is something amiss?” Investigating individuals’ competence in estimating swarm degradation

Abstract

Robotic swarms comprise component assets operating via local control algorithms which emulate natural swarming behaviors. Scientists are beginning to focus on the human-centered topic of human-swarm interaction. In a novel within-subjects design, we followed this comparatively nascent focus and investigated whether people can detect swarm degradations in assets flocking via consensus, their accuracy in estimating those degradations, and their confidence in those estimates. We also assessed open-ended responses to shed light on the strategies people may use to detect swarm degradations. Participants were recruited online and viewed 21 randomized simulations, each 30 seconds in duration with varying proportions of asset degradation. Results showed that the proportion of asset degradation did have an effect on the aforementioned criteria. Qualitative themes showed preliminary evidence that participants used common strategies to detect the time and degree of swarm degradation. However, we did not find evidence of a linear effect of the degradation manipulation on criteria of interest, which did not support our expectations. We discuss limitations and future research perspectives in detail, which we believe provide fodder for future work to investigate human-swarm interaction at a more granular level.

Keywords:

• Robotic swarms
• human-swarm interaction
• asset degradation
• flocking
• human-centered research

Acknowledgements

Distribution A. Approved for public release; distribution unlimited. AFRL-2021-0752; Cleared 9 March 2021.Opinions, findings, conclusions, and/or recommendations expressed in this article are those of the authors and do not necessarily reflect the views of the U.S. Air Force.

Data availability statement

The data that support the findings of this study are available from the first author, AC, upon reasonable request.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Alternatively, heading variance is easier to perceive when assets are nearer one another, which may have led to higher trust ratings associated with less compact swarms.

2 In fact, swarms with the highest proportion of degraded assets collected the greatest number of assets, yet participants had the lowest intentions to trust these swarms in future target foraging tasks. This shows that even though they were told the objective of the swarm was to collect targets, participants’ trust was influenced by the visual feature of asset degradation.

3 The denominator for this equation did not contain 398 cases from the 0% degradation trial as all non-responses were correct rejections (i.e., not responding when there was not a degradation present in the trial).

4 In contrast to the majority of work in psychology, recent work has shown that reaction times are more accurately modeled assuming a Weibull rather than a normal distribution (Fox, Houpt, and Tsang 2021). As such, using mean and standard deviation as measures of centrality and spread respectively—let alone conducting a t-test—are limited descriptive statistics for making generalizations about response time data. As such, assuming reaction times follow a normal distribution may be limitation, and results should be interpreted with caution.

5 Specifically, a user’s perception of a system’s reliability, coupled with their perception of that system’s algorithm consistency and (contextual) appropriateness, corresponds to the performance and process factors, respectively. These considerations have been leveraged in work on trust in robotic swarms (Hamdan et al. 2021) and may inform human-swarm interaction research centered on calibrating operator reliance.

Additional information

Funding

No potential conflict of interest was reported by the authors. This study was approved by the Air Force Research Laboratory Institutional Review Board.

Notes on contributors

August Capiola

August Capiola is a Research Psychologist in the Collaborative Teaming Section within the 711th Human Performance Wing at Wright-Patterson Air Force Base, Ohio. He earned his PhD in Human Factors and Industrial/Organizational Psychology at Wright State University. His research interests include trust in human-swarm interaction and swift trust in ad hoc teams.

Izz aldin Hamdan

Izz aldin Hamdan is a Research Analyst in the Collaborative Teaming Section within the 711th Human Performance Wing at Wright-Patterson Air Force Base, Ohio. He earned his MPS in Industrial/Organizational Psychology at George Mason University. His research interests include human-robot interaction, human-swarm interaction, and swift trust in ad hoc teams.

Elizabeth L. Fox

Elizabeth L. Fox is a Research Psychologist in the Sensory Systems Branch within the 711th Human Performance Wing at Wright-Patterson Air Force Base, Ohio. She earned her PhD in Human Factors and Industrial/Organizational Psychology at Wright State University. She develops neural- and behavioral-based models to inform the design of interfaces used in complex, multisensory domains.

Joseph B. Lyons

Joseph B. Lyons is a Principal Research Psychologist within the 711 Human Performance Wing at Wright-Patterson Air Force Base, Ohio. Dr. Lyons received his PhD in Industrial/Organizational Psychology from Wright State University in Dayton, Ohio, in 2005. Some of Dr. Lyons’ research interests include human-machine trust, interpersonal trust, human factors, and influence. Dr. Lyons has worked for the Air Force Research Laboratory as a civilian researcher since 2005, and between 2011 and 2013 he served as the Program Officer at the Air Force Office of Scientific Research where he created a basic research portfolio to study both interpersonal and human-machine trust as well as social influence. Dr. Lyons has published in a variety of peer-reviewed journals, and is an Associate Editor for the journal Military Psychology. Dr. Lyons is a Fellow of the American Psychological Association and the Society for Military Psychologists. Dr. Lyons can be contacted at: [email protected].

Katia Sycara

Katia Sycara is the Edward Fredkin Research Professor in Robotics at Carnegie Mellon University. She holds a PhD in Computer Science from Georgia Institute of Technology and a Doctorate Honoris Causa from the University of the Aegean. She is a Fellow of the Institute of Electronic and Electrical Engineers (IEEE), a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), recipient of the ACM/SIGART Agents Research Award, and recipient of the INFORMS Research Award, Group Decision and Negotiation Section. Her research interests include robotic swarms, trustworthy human-autonomy teaming, and AI/Machine Learning.

Michael Lewis

Michael Lewis is a professor in the Department of Informatics and Networked Systems and Intelligent Systems Programs in the School of Computing and Information at the University of Pittsburgh. He holds a PhD in Psychology from the Georgia Institute of Technology. His research focuses on human interaction with intelligent automation and modeling human operators in complex systems.