Human Factors in the Age of Artificial Intelligence (AI)

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 4311

Special Issue Editors


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Guest Editor
Information and Management for Innovation, i-University Tokyo, Tokyo 131-0044, Japan
Interests: mixed reality; robotics; virtual reality; human–computer interactions
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Guest Editor
Department of Communication and Media Technologies, University of Colombo School of Computing, Colombo, Sri Lanka
Interests: interactive 3D interfaces; unmanned aerial vehicles (UAVs); virtual environments; assistive technology; code analytics

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Guest Editor
Department of Communication and Media Technologies, University of Colombo School of Computing, Colombo, Sri Lanka
Interests: multisensory AR/VR; human–machine interactions; physical computing; magnetic user interfaces

Special Issue Information

Dear Colleagues,

As a result of the recent advancements of cyber-physical systems, machine learning, and the internet, we are moving towards a hyperconnected era, where humans, artificial intelligent systems, and machines are connected and collaborate together, this innovation having transformed our life by modifying our behaviours, interactions, and experiences. The purpose of this Special Issue is to publish recent advances in Human Factors in the Age of Artificial Intelligence. Our aim is to encourage researchers to publish experimental, theoretical, practical, and computational results in detail, so that results can be easily shared and reproduced. We seek submissions of original papers presenting novel research addressing Human Factors in the Age of Artificial Intelligence. There is no restriction on the length of papers, submissions must not currently be under consideration for publication in other venues. The topics of interest include, but are not limited to:

  • Human Factors in Artificial Intelligence;
  • Design and Development of Artificially Intelligent Systems;
  • Artificial Intelligence in Life Sciences;
  • Human–Robot Interactions/Social Robotics/Humanoid Robots;
  • AI in Autonomous Vehicles and UAVs;
  • AI and Intimate Relationships;
  • Integrated Intelligence;
  • AI-based Self-awareness;
  • AI-powered Sensing and Actuation;
  • AI-based Applications (IoT, Gaming, Medicine, Education, etc.);
  • AI in Advertising and Marketing;
  • AI and Global Economy;
  • AI and Society;
  • Ethics in AI.

Prof. Dr. Adrian David Cheok
Prof. Dr. Prasad Wimalaratne
Dr. Kasun Karunanayaka
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • human factors
  • human–robot interactions
  • artificial life
  • sensors and actuators
  • applications
  • prototypes
  • algorithms
  • simulations
  • ethics

Published Papers (2 papers)

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Research

15 pages, 867 KiB  
Article
Short-Term Demand Prediction of Shared Bikes Based on LSTM Network
by Yi Shi, Liumei Zhang, Shengnan Lu and Qiao Liu
Electronics 2023, 12(6), 1381; https://doi.org/10.3390/electronics12061381 - 14 Mar 2023
Cited by 4 | Viewed by 2352
Abstract
Shared transportation is widely used in current urban traffic. As a representative mode of transport, shared bikes have strong mobility and timeliness, so it is particularly critical to accurately predict the number of bikes used in an area every hour. In this paper, [...] Read more.
Shared transportation is widely used in current urban traffic. As a representative mode of transport, shared bikes have strong mobility and timeliness, so it is particularly critical to accurately predict the number of bikes used in an area every hour. In this paper, London bike-sharing data are selected as a data set to primarily analyze the impact of meteorological elements and time factors on bike-sharing demand. At the same time, it is important to use LSTM neural network models and popular machine learning models to predict demand for shared bikes at an hourly level. Through data analysis and visualization, the major elements affecting the bike-sharing demand are found to include humidity, peak hours, temperature, and other elements. The root mean squared error of the LSTM model is 314.17, the R2 score is as high as 0.922, and the error is small in comparison to other machine learning models. Through the evaluation indicators, it can be seen that the LSTM model has the smallest error between the prediction results and the true values of the compared machine learning methods, and the change trend of the model prediction result curve is basically consistent with the actual result curve. Full article
(This article belongs to the Special Issue Human Factors in the Age of Artificial Intelligence (AI))
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24 pages, 1466 KiB  
Article
Collaborative Autonomy: Human–Robot Interaction to the Test of Intelligent Help
by Filippo Cantucci and Rino Falcone
Electronics 2022, 11(19), 3065; https://doi.org/10.3390/electronics11193065 - 26 Sep 2022
Cited by 6 | Viewed by 1381
Abstract
A big challenge in human–robot interaction (HRI) is the design of autonomous robots that collaborate effectively with humans, exposing behaviors similar to those exhibited by humans when they interact with each other. Indeed, robots are part of daily life in multiple environments (i.e., [...] Read more.
A big challenge in human–robot interaction (HRI) is the design of autonomous robots that collaborate effectively with humans, exposing behaviors similar to those exhibited by humans when they interact with each other. Indeed, robots are part of daily life in multiple environments (i.e., cultural heritage sites, hospitals, offices, touristic scenarios and so on). In these contexts, robots have to coexist and interact with a wide spectrum of users not necessarily able or willing to adapt their interaction level to the kind requested by a machine: the users need to deal with artificial systems whose behaviors must be adapted as much as possible to the goals/needs of the users themselves, or more in general, to their mental states (beliefs, goals, plans and so on). In this paper, we introduce a cognitive architecture for adaptive and transparent human–robot interaction. The architecture allows a social robot to dynamically adjust its level of collaborative autonomy by restricting or expanding a delegated task on the basis of several context factors such as the mental states attributed to the human users involved in the interaction. This collaboration has to be based on different cognitive capabilities of the robot, i.e., the ability to build a user’s profile, to have a Theory of Mind of the user in terms of mental states attribution, to build a complex model of the context, intended both as a set of physical constraints and constraints due to the presence of other agents, with their own mental states. Based on the defined cognitive architecture and on the model of task delegation theorized by Castelfranchi and Falcone, the robot’s behavior is explainable by considering the abilities to attribute specific mental states to the user, the context in which it operates and its attitudes in adapting the level of autonomy to the user’s mental states and the context itself. The architecture has been implemented by exploiting the well known agent-oriented programming framework Jason. We provide the results of an HRI pilot study in which we recruited 26 real participants that have interacted with the humanoid robot Nao, widely used in HRI scenarios. The robot played the role of a museum assistant with the main goal to provide the user the most suitable museum exhibition to visit. Full article
(This article belongs to the Special Issue Human Factors in the Age of Artificial Intelligence (AI))
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