scholarly journals Fuzzy linguistic descriptions for execution trace comprehension and their application in an introductory course in artificial intelligence

2019 ◽  
Vol 37 (6) ◽  
pp. 8397-8415
Author(s):  
Clemente Rubio-Manzano ◽  
Tomás Lermanda Senoceaín ◽  
Claudia Martinez-Araneda ◽  
Christian Vidal-Castro ◽  
Alejandra Segura-Navarrete
Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1566
Author(s):  
Ruben Heradio ◽  
David Fernandez-Amoros ◽  
Cristina Cerrada ◽  
Manuel J. Cobo

Decisions concerning crucial and complicated problems are seldom made by a single person. Instead, they require the cooperation of a group of experts in which each participant has their own individual opinions, motivations, background, and interests regarding the existing alternatives. In the last 30 years, much research has been undertaken to provide automated assistance to reach a consensual solution supported by most of the group members. Artificial intelligence techniques are commonly applied to tackle critical group decision-making difficulties. For instance, experts’ preferences are often vague and imprecise; hence, their opinions are combined using fuzzy linguistic approaches. This paper reports a bibliometric analysis of the ample literature published in this regard. In particular, our analysis: (i) shows the impact and upswing publication trend on this topic; (ii) identifies the most productive authors, institutions, and countries; (iii) discusses authors’ and journals’ productivity patterns; and (iv) recognizes the most relevant research topics and how the interest on them has evolved over the years.


AI Magazine ◽  
2014 ◽  
Vol 35 (4) ◽  
pp. 125-126
Author(s):  
Marie DesJardins

his column describes my experience with using a new classroom space (the ACTIVE Center), which was designed to facilitate group-based active learning and problem solving, to teach an introductory artificial intelligence course. By restructuring the course into a format that was roughly half lecture and half small-group problem-solving, I was able to significantly increase student engagement, their understanding and retention of difficult concepts, and my own enjoyment in teaching the class.


2020 ◽  
Author(s):  
Christiane Gresse von Wangenheim ◽  
Lívia S. Marques ◽  
Jean C. R. Hauck

Although Machine Learning (ML) is integrated today into various aspects of our lives, few understand the technology behind it. This presents new challenges to extend computing education early on including ML concepts in order to help students to understand its potential and limits and empowering them to become creators of intelligent solutions. Therefore, we developed an introductory course to teach basic ML concepts, such as fundamentals of neural networks, learning as well as limitations and ethical concerns in alignment with the K-12 Guidelines for Artificial Intelligence. It also teaches the application of these concepts, by guiding the students to develop a first image recognition model of recycling trash using Google Teachable Machine. In order to promote ML education, the interactive course is available online in Brazilian Portuguese to be used as an extracurricular course or in an interdisciplinary way as part of science classes covering recycling topics.


Author(s):  
LICONG CUI ◽  
YONGMING LI ◽  
XIAOHONG ZHANG

In this paper, we generalize Ying's model of linguistic quantifiers [M.S. Ying, Linguistic quantifiers modeled by Sugeno integrals, Artificial Intelligence, 170 (2006) 581-606] to intuitionistic linguistic quantifiers. An intuitionistic linguistic quantifier is represented by a family of intuitionistic fuzzy-valued fuzzy measures and the intuitionistic truth value (the degrees of satisfaction and non-satisfaction) of a quantified proposition is calculated by using intuitionistic fuzzy-valued fuzzy integral. Description of a quantifier by intuitionistic fuzzy-valued fuzzy measures allows us to take into account differences in understanding the meaning of the quantifier by different persons. If the intuitionistic fuzzy linguistic quantifiers are taken to be linguistic fuzzy quantifiers, then our model reduces to Ying's model. Some excellent logical properties of intuitionistic linguistic quantifiers are obtained including a prenex norm form theorem. A simple example is presented to illustrate the use of intuitionistic linguistic quantifiers.


2010 ◽  
Vol 20 (1) ◽  
pp. 10-14 ◽  
Author(s):  
Evelyn R. Klein ◽  
Barbara J. Amster

Abstract A study by Yaruss and Quesal (2002), based on responses from 134 of 239 ASHA accredited graduate programs, indicated that approximately 25% of graduate programs in the United States allow students to earn their degree without having coursework in fluency disorders and 66% of programs allow students to graduate without clinical experience treating people who stutter (PWS). It is not surprising that many clinicians report discomfort in treating PWS. This cross-sectional study compares differences in beliefs about the cause of stuttering between freshman undergraduate students enrolled in an introductory course in communicative disorders and graduate students enrolled and in the final weeks of a graduate course in fluency disorders.


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