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Applied Sciences
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Human Machine Interaction
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Human Machine Interaction

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (15 June 2021) | Viewed by 17085

Special Issue Editors

Prof. Dr. Jean Baratgin

E-Mail Website
Guest Editor
Lab Cognit Humaine & Artificielle CHArt, Université Paris 8, 93526 Saint-Denis, France
Interests: rationality; uncertain reasoning; judgement; decision-making; belief revision; cognition; human-machine interaction; experimental research
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Thierry Karsenti

E-Mail Website
Guest Editor
University of Montreal, Canada
Interests: quality of pedagogy at the university level; pedagogical integration of new technologies; pedagogical teaching practices; open and distance learning; and motivation; AI and teaching and learning; education and robotics
Prof. Dr. Alain Jaillet

E-Mail Website
Guest Editor
University of Paris Cergy, France
Interests: semiotics; distance learning; new technology and education; simulation and learning

Special Issue Information

Dear Colleagues,

Physical or virtual interactions with “artificial” digital machines (robots, conversational agents, etc.) are becoming more and more important in our everyday life in more and more varied contexts (education, teleworking, coaching, help experts, etc.). However, in many situations, these “artificial interactions” remain frustrating experiences for many users compared to naturally easier human–human interactions. Many implicit factors (context, trust, social and linguistic pragmatics, knowledge and representations, etc.) enabling interactive cooperation between humans are rarely taken into account by artificial agents interacting with humans.

This Special Issue will focus on all the means to improve these current collaborative limits. It will be particularly interested in the field of user representations and knowledge (both from the point of view of the modelization of the user’s representations and the design of tools and devices to improve them), to the epistemic aspects of interactions with machines, cognitive engineering and will also be interested in concrete examples of improving interaction with people with specific needs (such as pupils, people with disabilities, etc.). We are especially interested in the use of tools such as chatbots and robots in the topics mentioned above. 

We welcome research papers from engineering new methods, from professional new use, and from new experimental results in cognitive psychology, which will contribute to the improvement of human–machine interactions.

Prof. Dr. Jean Baratgin
Prof. Dr. Thierry Karsenti
Prof. Dr. Alain Jaillet
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. Applied Sciences 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

  • chatbots
  • tablets
  • computers
  • robot
  • interaction
  • conversational agent
  • pragmatics
  • cooperation
  • trust
  • Turing test
  • education
  • disabilities
  • information and communication technology

Published Papers (4 papers)

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10 pages, 296 KiB  
Article
Text-Based Emotion Recognition in English and Polish for Therapeutic Chatbot
by Artur Zygadło, Marek Kozłowski and Artur Janicki
Appl. Sci. 2021, 11(21), 10146; https://doi.org/10.3390/app112110146 - 29 Oct 2021
Cited by 12 | Viewed by 3024
Abstract
In this article, we present the results of our experiments on sentiment and emotion recognition for English and Polish texts, aiming to work in the context of a therapeutic chatbot. We created a dedicated dataset by adding samples of neutral texts to an [...] Read more.
In this article, we present the results of our experiments on sentiment and emotion recognition for English and Polish texts, aiming to work in the context of a therapeutic chatbot. We created a dedicated dataset by adding samples of neutral texts to an existing English-language emotion-labeled corpus. Next, using neural machine translation, we developed a Polish version of the English database. A bilingual, parallel corpus created in this way, named CORTEX (CORpus of Translated Emotional teXts), labeled with three sentiment polarity classes and nine emotion classes, was used for experiments on classification. We employed various classifiers: Naïve Bayes, Support Vector Machines, fastText, and BERT. The results obtained were satisfactory: we achieved the best scores for the BERT-based models, which yielded accuracy of over 90% for sentiment (3-class) classification and almost 80% for emotion (9-class) classification. We compared the results for both languages and discussed the differences. Both the accuracy and the F1-scores for Polish turned out to be slightly inferior to those for English, with the highest difference visible for BERT. Full article
(This article belongs to the Special Issue Human Machine Interaction)
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10 pages, 293 KiB  
Article
Contextual Information Helps Understand Messages Written with Textisms
by Baptiste Jacquet, Caline Jaraud, Frank Jamet, Sabine Guéraud and Jean Baratgin
Appl. Sci. 2021, 11(11), 4853; https://doi.org/10.3390/app11114853 - 25 May 2021
Cited by 5 | Viewed by 2022
Abstract
The present study investigated the influence of the use of textisms, a form of written language used in phone-mediated conversations, on the cognitive cost of French participants in an online conversation. Basing our thinking on the relevance theory of Sperber and Wilson, we [...] Read more.
The present study investigated the influence of the use of textisms, a form of written language used in phone-mediated conversations, on the cognitive cost of French participants in an online conversation. Basing our thinking on the relevance theory of Sperber and Wilson, we tried to assess whether knowing the context and topic of a conversation can produce a significant decrease in the cognitive cost required to read messages written in textism by giving additional clues to help infer the meaning of these messages. In order to do so, participants played the judges in a Turing test between a normal conversation (written with the traditional writing style) and a conversation in which the experimenter was conversing with textisms, in a random order. The results indicated that participants answered messages written in textism faster when they were in the second conversation. We concluded that prior knowledge about the conversation can help interpret the messages written in textisms by decreasing the cognitive cost required to infer their meaning. Full article
(This article belongs to the Special Issue Human Machine Interaction)
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13 pages, 674 KiB  
Article
New Approach in Human-AI Interaction by Reinforcement-Imitation Learning
by Neda Navidi and Rene Landry, Jr.
Appl. Sci. 2021, 11(7), 3068; https://doi.org/10.3390/app11073068 - 30 Mar 2021
Cited by 4 | Viewed by 3226
Abstract
Reinforcement Learning (RL) provides effective results with an agent learning from a stand-alone reward function. However, it presents unique challenges with large amounts of environment states and action spaces, as well as in the determination of rewards. Imitation Learning (IL) offers a promising [...] Read more.
Reinforcement Learning (RL) provides effective results with an agent learning from a stand-alone reward function. However, it presents unique challenges with large amounts of environment states and action spaces, as well as in the determination of rewards. Imitation Learning (IL) offers a promising solution for those challenges using a teacher. In IL, the learning process can take advantage of human-sourced assistance and/or control over the agent and environment. A human teacher and an agent learner are considered in this study. The teacher takes part in the agent’s training towards dealing with the environment, tackling a specific objective, and achieving a predefined goal. This paper proposes a novel approach combining IL with different types of RL methods, namely, state-action-reward-state-action (SARSA) and Asynchronous Advantage Actor–Critic Agents (A3C), to overcome the problems of both stand-alone systems. How to effectively leverage the teacher’s feedback—be it direct binary or indirect detailed—for the agent learner to learn sequential decision-making policies is addressed. The results of this study on various OpenAI-Gym environments show that this algorithmic method can be incorporated with different combinations, and significantly decreases both human endeavors and tedious exploration process. Full article
(This article belongs to the Special Issue Human Machine Interaction)
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Other

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25 pages, 2692 KiB  
Systematic Review
Smart Toys in Early Childhood and Primary Education: A Systematic Review of Technological and Educational Affordances
by Vassilis Komis, Christofors Karachristos, Despina Mourta, Konstantina Sgoura, Anastasia Misirli and Alain Jaillet
Appl. Sci. 2021, 11(18), 8653; https://doi.org/10.3390/app11188653 - 17 Sep 2021
Cited by 5 | Viewed by 7344
Abstract
The present paper presents a systematic review of the last 30 years that concerns records on Smart Toys and focuses on toys regarding early childhood and primary education children (3–12 years old). This paper aims to analyse and categorise smart toys (50 articles) [...] Read more.
The present paper presents a systematic review of the last 30 years that concerns records on Smart Toys and focuses on toys regarding early childhood and primary education children (3–12 years old). This paper aims to analyse and categorise smart toys (50 articles) in terms of their technological and educational affordances. The results show that the toys are designed based on four main technological affordances and their combinations. The educational affordances of smart toys are studied in terms of different use modes and their learning objectives aimed to identify specific objectives in different subjects and objectives based on transversal competencies such as problem solving, spatial thinking, computational thinking, collaboration and symbolic thinking. Finally, with the multiple correspondence analysis, the correlations between smart toys’ individual technological and educational affordances are grouped with the evolution of affordances related to their development date. In conclusion, in recent years, smart toys concern special sciences (programming) and some 21st-century skills (STEM and computational thinking). In contrast, in the first 20 years, the interest focused more on transverse skills, such as collaboration, emotional thinking, symbolic thinking, story-telling and problem solving. Full article
(This article belongs to the Special Issue Human Machine Interaction)
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