scholarly journals Transparency in Autonomous Teammates

2019 ◽  
Vol 14 (2) ◽  
pp. 174-190 ◽  
Author(s):  
April Rose Panganiban ◽  
Gerald Matthews ◽  
Michael D. Long
Keyword(s):  
Social Cues ◽  
Social Benefits ◽  
Human Machine Interaction ◽  
Team Collaboration ◽  
Task Setting ◽  
The Social ◽  
Team Environment ◽  
Near Term ◽  
Occupational Settings ◽  
Machine Interaction

Human–Machine teaming is a very near term standard for many occupational settings and still requires considerations for the design of autonomous teammates (ATs). Transparency of system processes is important for human–machine interaction and reliance but standards for its implementation are still being explored. Embedding social cues is a potential design approach, which may capture the social benefits of a team environment, yet vary with task setting. The current study examined the manipulation of transparency of benevolent intent from an AT within a piloting task requiring suppression of enemy defenses. Specifically, the benevolent AT maintained task communication as in a neutral condition, but included messages of support and awareness of errors. Benevolent communication reduced reported workload and increased reported team collaboration, indicating that this team intent was beneficial. In addition, trust and acceptance of the AT were rated higher by individuals tasked with depending on the system to protect them from missile threats. The need for information from ATs is beneficial, however may vary depending on team type.

scholarly journals A THEORETICAL AND PRACTICAL COMPREHENSIVE FRAMEWORK FOR ARTIFICIAL INTELLIGENCE ANDSOCIO-PSYCHOLOGICAL CHALLENGES

2020 ◽  
Vol 13(62) (1) ◽  
pp. 33-40
Author(s):  
L. POGAN ◽  
R.I. POPA
Keyword(s):  
Social Sciences ◽  
Artificial Intelligence ◽  
Theoretical Perspective ◽  
Research Trends ◽  
Psychological Perspective ◽  
Human Machine Interaction ◽  
Personality Variables ◽  
The Social ◽  
Comprehensive Framework ◽  
Machine Interaction

The present paper aims to highlight the major up to date findings and research trends on the topic of artificial intelligence (AI) from a theoretical perspective, analysed in association with the human-machine interaction challenges. The existing research and scholarly literature display quite heterogenous approaches on these topics, and only a few of them tackle the social sciences view on the matter. In conclusion, there is a need for a more specific approach from the socio-psychological perspective, especially in topics concerning human centricity, human training at work, personality variables and other indicators when using AI devices.

Does context matter? Effects of robot appearance and reliability on social attention differs based on lifelikeness of gaze task

2020 ◽  
Author(s):  
Abdulaziz Abubshait ◽  
Patrick P. Weis ◽  
Eva Wiese
Keyword(s):  
Cognitive Processing ◽  
Social Cognitive ◽  
Physical Appearance ◽  
Mental States ◽  
Social Attention ◽  
Social Cues ◽  
Social Signals ◽  
Mind Perception ◽  
The Social ◽  
Behavioral Features

Social signals, such as changes in gaze direction, are essential cues to predict others’ mental states and behaviors (i.e., mentalizing). Studies show that humans can mentalize with non-human agents when they perceive a mind in them (i.e., mind perception). Robots that physically and/or behaviorally resemble humans likely trigger mind perception, which enhances the relevance of social cues and improves social-cognitive performance. The current ex-periments examine whether the effect of physical and behavioral influencers of mind perception on social-cognitive processing is modulated by the lifelikeness of a social interaction. Participants interacted with robots of varying degrees of physical (humanlike vs. robot-like) and behavioral (reliable vs. random) human-likeness while the lifelikeness of a social attention task was manipulated across five experiments. The first four experiments manipulated lifelikeness via the physical realism of the robot images (Study 1 and 2), the biological plausibility of the social signals (Study 3), and the plausibility of the social con-text (Study 4). They showed that humanlike behavior affected social attention whereas appearance affected mind perception ratings. However, when the lifelikeness of the interaction was increased by using videos of a human and a robot sending the social cues in a realistic environment (Study 5), social attention mechanisms were affected both by physical appearance and behavioral features, while mind perception ratings were mainly affected by physical appearance. This indicates that in order to understand the effect of physical and behavioral features on social cognition, paradigms should be used that adequately simulate the lifelikeness of social interactions.

Concise CNN model for face expression recognition

2021 ◽  
pp. 1-9
Author(s):  
Harshadkumar B. Prajapati ◽  
Ankit S. Vyas ◽  
Vipul K. Dabhi
Keyword(s):  
Network Architecture ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Face Expression ◽  
Computational Overhead ◽  
Average Accuracy ◽  
Proposed Model ◽  
Face Expression Recognition ◽  
Expression Classification ◽  
Machine Interaction

Face expression recognition (FER) has gained very much attraction to researchers in the field of computer vision because of its major usefulness in security, robotics, and HMI (Human-Machine Interaction) systems. We propose a CNN (Convolutional Neural Network) architecture to address FER. To show the effectiveness of the proposed model, we evaluate the performance of the model on JAFFE dataset. We derive a concise CNN architecture to address the issue of expression classification. Objective of various experiments is to achieve convincing performance by reducing computational overhead. The proposed CNN model is very compact as compared to other state-of-the-art models. We could achieve highest accuracy of 97.10% and average accuracy of 90.43% for top 10 best runs without any pre-processing methods applied, which justifies the effectiveness of our model. Furthermore, we have also included visualization of CNN layers to observe the learning of CNN.

scholarly journals A Bibliometric Analysis of Human-Machine Interaction Methodology for Electric-Powered Wheelchairs Driving from 1998 to 2020

2021 ◽  
Vol 18 (14) ◽  
pp. 7567
Author(s):  
Xiaochen Zhang ◽  
Lanxin Hui ◽  
Linchao Wei ◽  
Fuchuan Song ◽  
Fei Hu
Keyword(s):  
The Elderly ◽  
Research Field ◽  
Future Research ◽  
Human Machine Interaction ◽  
Research Directions ◽  
The Past ◽  
Power Wheelchairs ◽  
The Disabled ◽  
Future Research Directions ◽  
Machine Interaction

Electric power wheelchairs (EPWs) enhance the mobility capability of the elderly and the disabled, while the human-machine interaction (HMI) determines how well the human intention will be precisely delivered and how human-machine system cooperation will be efficiently conducted. A bibliometric quantitative analysis of 1154 publications related to this research field, published between 1998 and 2020, was conducted. We identified the development status, contributors, hot topics, and potential future research directions of this field. We believe that the combination of intelligence and humanization of an EPW HMI system based on human-machine collaboration is an emerging trend in EPW HMI methodology research. Particular attention should be paid to evaluating the applicability and benefits of the EPW HMI methodology for the users, as well as how much it contributes to society. This study offers researchers a comprehensive understanding of EPW HMI studies in the past 22 years and latest trends from the evolutionary footprints and forward-thinking insights regarding future research.

Holistic, Context-sensitive Human-machine Interaction for Automated Vehicles

ATZ worldwide ◽  
2021 ◽  
Vol 123 (3) ◽  
pp. 46-49
Author(s):  
Tobias Hesse ◽  
Michael Oehl ◽  
Uwe Drewitz ◽  
Meike Jipp
Keyword(s):  
Automated Vehicles ◽  
Human Machine Interaction ◽  
Context Sensitive ◽  
Machine Interaction

scholarly journals Radiology Community Attitude in Saudi Arabia about the Applications of Artificial Intelligence in Radiology

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 834
Author(s):  
Magbool Alelyani ◽  
Sultan Alamri ◽  
Mohammed S. Alqahtani ◽  
Alamin Musa ◽  
Hajar Almater ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Saudi Arabia ◽  
Vital Role ◽  
Human Machine Interaction ◽  
The Future ◽  
Level Of Knowledge ◽  
Electronic Questionnaires ◽  
Machine Interaction ◽  
Applications Of Ai

Artificial intelligence (AI) is a broad, umbrella term that encompasses the theory and development of computer systems able to perform tasks normally requiring human intelligence. The aim of this study is to assess the radiology community’s attitude in Saudi Arabia toward the applications of AI. Methods: Data for this study were collected using electronic questionnaires in 2019 and 2020. The study included a total of 714 participants. Data analysis was performed using SPSS Statistics (version 25). Results: The majority of the participants (61.2%) had read or heard about the role of AI in radiology. We also found that radiologists had statistically different responses and tended to read more about AI compared to all other specialists. In addition, 82% of the participants thought that AI must be included in the curriculum of medical and allied health colleges, and 86% of the participants agreed that AI would be essential in the future. Even though human–machine interaction was considered to be one of the most important skills in the future, 89% of the participants thought that it would never replace radiologists. Conclusion: Because AI plays a vital role in radiology, it is important to ensure that radiologists and radiographers have at least a minimum understanding of the technology. Our finding shows an acceptable level of knowledge regarding AI technology and that AI applications should be included in the curriculum of the medical and health sciences colleges.

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