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26 pages, 1001 KiB

Open AccessArticle

KIDE4I: A Generic Semantics-Based Task-Oriented Dialogue System for Human-Machine Interaction in Industry 5.0

by Cristina Aceta, Izaskun Fernández and Aitor Soroa

Appl. Sci. 2022, 12(3), 1192; https://doi.org/10.3390/app12031192 - 24 Jan 2022

Cited by 17 | Viewed by 2861

In Industry 5.0, human workers and their wellbeing are placed at the centre of the production process. In this context, task-oriented dialogue systems allow workers to delegate simple tasks to industrial assets while working on other, more complex ones. The possibility of naturally [...] Read more.

In Industry 5.0, human workers and their wellbeing are placed at the centre of the production process. In this context, task-oriented dialogue systems allow workers to delegate simple tasks to industrial assets while working on other, more complex ones. The possibility of naturally interacting with these systems reduces the cognitive demand to use them and triggers acceptation. Most modern solutions, however, do not allow a natural communication, and modern techniques to obtain such systems require large amounts of data to be trained, which is scarce in these scenarios. To overcome these challenges, this paper presents KIDE4I (Knowledge-drIven Dialogue framEwork for Industry), a semantic-based task-oriented dialogue system framework for industry that allows workers to naturally interact with industrial systems, is easy to adapt to new scenarios and does not require great amounts of data to be constructed. This work also reports the process to adapt KIDE4I to new scenarios. To validate and evaluate KIDE4I, it has been adapted to four use cases that are relevant to industrial scenarios following the described methodology, and two of them have been evaluated through two user studies. The system has been considered as accurate, useful, efficient, not demanding cognitively, flexible and fast. Furthermore, subjects view the system as a tool to improve their productivity and security while carrying out their tasks. Full article

(This article belongs to the Special Issue Human-Computer Interaction: Theory and Practice)

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12 pages, 6257 KiB

Open AccessArticle

Topic Recommendation to Expand Knowledge and Interest in Question-and-Answer Agents

by Albert Deok-Young Yang, Yeo-Gyeong Noh and Jin-Hyuk Hong

Appl. Sci. 2021, 11(22), 10600; https://doi.org/10.3390/app112210600 - 11 Nov 2021

Viewed by 1993

Abstract

By providing a high degree of freedom to explore information, QA (question and answer) agents in museums are expected to help visitors gain knowledge on a range of exhibits. Since information exploration with a QA agent often involves a series of interactions, proper [...] Read more.

By providing a high degree of freedom to explore information, QA (question and answer) agents in museums are expected to help visitors gain knowledge on a range of exhibits. Since information exploration with a QA agent often involves a series of interactions, proper guidance is required to support users as they find out what they want to know and broaden their knowledge. In this paper, we validate topic recommendation strategies of system-initiative QA agents that suggest multiple topics in different ways to influence users’ information exploration, and to help users proceed to deeper levels in topics on the same subject, to offer them topics on various subjects, or to provide them with selections at random. To examine how different recommendations influence users’ experience, we have conducted a user study with 50 participants which has shown that providing recommendations on various subjects expands their interest on subjects, supports longer conversations, and increases willingness to use QA agents in the future. Full article

(This article belongs to the Special Issue Human-Computer Interaction: Theory and Practice)

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27 pages, 8139 KiB

Open AccessArticle

From the Concept of Being “the Boss” to the Idea of Being “a Team”: The Adaptive Co-Pilot as the Enabler for a New Cooperative Framework

by Mauricio Marcano, Fabio Tango, Joseba Sarabia, Andrea Castellano, Joshué Pérez, Eloy Irigoyen and Sergio Díaz

Appl. Sci. 2021, 11(15), 6950; https://doi.org/10.3390/app11156950 - 28 Jul 2021

Cited by 13 | Viewed by 2120

Abstract

The “classical” SAE LoA for automated driving can present several drawbacks, and the SAE-L2 and SAE-L3, in particular, can lead to the so-called “irony of automation”, where the driver is substituted by the artificial system, but is still regarded as a “supervisor” or [...] Read more.

The “classical” SAE LoA for automated driving can present several drawbacks, and the SAE-L2 and SAE-L3, in particular, can lead to the so-called “irony of automation”, where the driver is substituted by the artificial system, but is still regarded as a “supervisor” or as a “fallback mechanism”. To overcome this problem, while taking advantage of the latest technology, we regard both human and machine as members of a unique team that share the driving task. Depending on the available resources (in terms of driver’s status, system state, and environment conditions) and considering that they are very dynamic, an adaptive assignment of authority for each member of the team is needed. This is achieved by designing a technology enabler, constituted by the intelligent and adaptive co-pilot. It comprises (1) a lateral shared controller based on NMPC, which applies the authority, (2) an arbitration module based on FIS, which calculates the authority, and (3) a visual HMI, as an enabler of trust in automation decisions and actions. The benefits of such a system are shown in this paper through a comparison of the shared control driving mode, with manual driving (as a baseline) and lane-keeping and lane-centering (as two commercial ADAS). Tests are performed in a use case where support for a distracted driver is given. Quantitative and qualitative results confirm the hypothesis that shared control offers the best balance between performance, safety, and comfort during the driving task. Full article

(This article belongs to the Special Issue Human-Computer Interaction: Theory and Practice)

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31 pages, 5790 KiB

Open AccessArticle

Safe Vehicle Trajectory Planning in an Autonomous Decision Support Framework for Emergency Situations

by Wei Xu, Rémi Sainct, Dominique Gruyer and Olivier Orfila

Appl. Sci. 2021, 11(14), 6373; https://doi.org/10.3390/app11146373 - 09 Jul 2021

Cited by 13 | Viewed by 2812

Abstract

For a decade, researchers have focused on the development and deployment of road automated mobility. In the development of autonomous driving embedded systems, several stages are required. The first one deals with the perception layers. The second one is dedicated to the risk [...] Read more.

For a decade, researchers have focused on the development and deployment of road automated mobility. In the development of autonomous driving embedded systems, several stages are required. The first one deals with the perception layers. The second one is dedicated to the risk assessment, the decision and strategy layers and the optimal trajectory planning. The last stage addresses the vehicle control/command. This paper proposes an efficient solution to the second stage and improves a virtual Cooperative Pilot (Co-Pilot) already proposed in 2012. This paper thus introduces a trajectory planning algorithm for automated vehicles (AV), specifically designed for emergency situations and based on the Autonomous Decision-Support Framework (ADSF) of the EU project Trustonomy. This algorithm is an extended version of Elastic Band (EB) with no fixed final position. A set of trajectory nodes is iteratively deduced from obstacles and constraints, thus providing flexibility, fast computation, and physical realism. After introducing the project framework for risk management and the general concept of ADSF, the emergency algorithm is presented and tested under Matlab software. Finally, the Decision-Support framework is implemented under RTMaps software and demonstrated within Pro-SiVIC, a realistic 3D simulation environment. Both the previous virtual Co-Pilot and the new emergency algorithm are combined and used in a near-accident situation and shown in different risky scenarios. Full article

(This article belongs to the Special Issue Human-Computer Interaction: Theory and Practice)

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15 pages, 1927 KiB

Open AccessArticle

Self-Learning Mechanism for Mobile Game Adjustment towards a Player

by Milana Bojanić and Goran Bojanić

Appl. Sci. 2021, 11(10), 4412; https://doi.org/10.3390/app11104412 - 13 May 2021

Cited by 3 | Viewed by 1728

Abstract

Mobile app markets have faced huge expansion during the last decade. Among different apps, games represent a large portion with a wide range of game categories having consumers in all age groups. To make a mobile game suitable for different age categories, it [...] Read more.

Mobile app markets have faced huge expansion during the last decade. Among different apps, games represent a large portion with a wide range of game categories having consumers in all age groups. To make a mobile game suitable for different age categories, it is necessary to adjust difficulty levels in such a way to keep the game challenging for different players with different playing skills. The mobile app puzzle game Wonderful Animals has been developed consisting of puzzles, find pairs and find differences game (available on the Google Play Store). The game testing was conducted on a group of 40 players by recording game level completion time and conducting a survey of their subjective evaluation of completed level difficulty. The study aimed to find a mechanism to adjust game level difficulty to the individual player taking into account the player’s achievements on previously played games. A pseudo-algorithm for self-learning mechanism is presented, enabling level difficulty adaptation to the player. Furthermore, player classification into three classes using neural networks is suggested in order to offer a user-specific playing environment. The experimental results show that the average recognition rate of the player class was 96.1%. Full article

(This article belongs to the Special Issue Human-Computer Interaction: Theory and Practice)

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20 pages, 2685 KiB

Open AccessArticle

Cognitive Modeling of Task Switching in Discretionary Multitasking Based on the ACT-R Cognitive Architecture

by Hyungseok Oh, Yongdeok Yun and Rohae Myung

Appl. Sci. 2021, 11(9), 3967; https://doi.org/10.3390/app11093967 - 27 Apr 2021

Cited by 4 | Viewed by 2453

Abstract

Discretionary multitasking has emerged as a prevalent and important domain in research on human–computer interaction. Studies on modeling based on cognitive architectures such as ACT-R to gain insight into and predict human behavior in multitasking are critically important. However, studies on ACT-R modeling [...] Read more.

Discretionary multitasking has emerged as a prevalent and important domain in research on human–computer interaction. Studies on modeling based on cognitive architectures such as ACT-R to gain insight into and predict human behavior in multitasking are critically important. However, studies on ACT-R modeling have mainly focused on concurrent and sequential multitasking, including scheduled task switching. Therefore, in this study, an ACT-R cognitive model of task switching in discretionary multitasking was developed to provide an integrated account of when and how humans decide on switching tasks. Our model contains a symbolic structure and subsymbolic equations that represent the cognitive process of task switching as self-interruption by the imposed demands and a decision to switch. To validate our model, it was applied to an illustrative dual task, including a memory game and a subitizing task, and the results were compared with human data. The results demonstrate that our model can provide a relatively accurate representation, in terms of task-switching percent just after the subtask, the number of task-switching during the subtask, and performance time depending on the task difficulty level; it exhibits enhanced performance in predicting human behavior in multitasking and demonstrates how ACT-R facilitates accounts of voluntary task switching. Full article

(This article belongs to the Special Issue Human-Computer Interaction: Theory and Practice)

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36 pages, 648 KiB

Open AccessArticle

Where We Come from and Where We Are Going: A Systematic Review of Human Factors Research in Driving Automation

by Anna-Katharina Frison, Yannick Forster, Philipp Wintersberger, Viktoria Geisel and Andreas Riener

Appl. Sci. 2020, 10(24), 8914; https://doi.org/10.3390/app10248914 - 14 Dec 2020

Cited by 14 | Viewed by 4079

Abstract

During the last decade, research has brought forth a large amount of studies that investigated driving automation from a human factor perspective. Due to the multitude of possibilities for the study design with regard to the investigated constructs, data collection methods, and evaluated [...] Read more.

During the last decade, research has brought forth a large amount of studies that investigated driving automation from a human factor perspective. Due to the multitude of possibilities for the study design with regard to the investigated constructs, data collection methods, and evaluated parameters, at present, the pool of findings is heterogeneous and nontransparent. This literature review applied a structured approach, where five reviewers investigated n = 161 scientific papers of relevant journals and conferences focusing on driving automation between 2010 and 2018. The aim was to present an overview of the status quo of existing methodological approaches and investigated constructs to help scientists in conducting research with established methods and advanced study setups. Results show that most studies focused on safety aspects, followed by trust and acceptance, which were mainly collected through self-report measures. Driving/Take-Over performance also marked a significant portion of the published papers; however, a wide range of different parameters were investigated by researchers. Based on our insights, we propose a set of recommendations for future studies. Amongst others, this includes validation of existing results on real roads, studying long-term effects on trust and acceptance (and of course other constructs), or triangulation of self-reported and behavioral data. We furthermore emphasize the need to establish a standardized set of parameters for recurring use cases to increase comparability. To assure a holistic contemplation of automated driving, we moreover encourage researchers to investigate other constructs that go beyond safety. Full article

(This article belongs to the Special Issue Human-Computer Interaction: Theory and Practice)

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20 pages, 2843 KiB

Open AccessArticle

A Heuristic Method for Evaluating Accessibility in Web-Based Serious Games for Users with Low Vision

by Luis Salvador-Ullauri, Patricia Acosta-Vargas, Mario Gonzalez and Sergio Luján-Mora

Appl. Sci. 2020, 10(24), 8803; https://doi.org/10.3390/app10248803 - 09 Dec 2020

Cited by 6 | Viewed by 2889

Abstract

Nowadays, serious games have become a beneficial resource in the learning process; they are part of our culture and promote social inclusion. Designing accessible serious games is a complete challenge, even more for non-experts. Most existing serious games do not meet accessibility standards [...] Read more.

Nowadays, serious games have become a beneficial resource in the learning process; they are part of our culture and promote social inclusion. Designing accessible serious games is a complete challenge, even more for non-experts. Most existing serious games do not meet accessibility standards because of a lack of methods that include standards and help create more accessible serious games. For this reason, our research presents a heuristic method with three modifications to Giorgio Brajnik’s barrier walkthrough method and based on the Web Content Accessibility Guidelines 2.1 (WCAG 2.1). We defined 28 barriers for the users with low vision and the related impact and persistence variables by defining severity ranges to evaluate accessibility. This method allows measuring the accessibility of web-based serious games; the method proposed in this article can be a good help for non-experts. As a case study, this heuristic method was applied to 40 web-based serious games. The evaluators concluded that serious games should apply WCAG 2.1 to achieve an adequate and inclusive accessibility level. However, this study has limitations; the heuristic method depends on the evaluators’ experience. This work can contribute to studies related to accessibility heuristics in serious games; it can also help construct a software tool that applies WCAG 2.1 and helps experts and non-experts evaluate accessibility in serious games. Full article

(This article belongs to the Special Issue Human-Computer Interaction: Theory and Practice)

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