Decision Support Systems: Challenges and Solutions

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 10869

Special Issue Editors


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Guest Editor
GECAD, Institute of Engineering, Polytechnic Institute of Porto, 4200-072 Porto, Portugal
Interests: Artificial Intelligence; multi-agent systems; emotional agents; persuasive argumentation; group decision-support systems
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
GECAD, Institute of Engineering, Polytechnic Institute of Porto, 4200-072 Porto, Portugal
Interests: artificial intelligence; group decision support systems; argumentation-based dialogues; affective computing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Valencian Research Institute for Artificial Intelligence (VRAIN), Universitat Politècnica de València (UPV), 46022 València, Spain
Interests: Artificial Intelligence; multi-agent planning; game theory; argumentation; decision support systems; recommender systems

Special Issue Information

Dear Colleagues,

Decision support systems are used in different fields to support individuals and groups making decisions. They are expected to facilitate decision-making processes while enhancing the quality of the decisions. However, in an increasingly dynamic and complex world, the task of supporting decision-makers is extremely challenging. It is necessary to provide the appropriate information according to the decision-makers’ needs and to make recommendations that are understandable. In addition, when decisions are performed in group, it is important to consider the heterogeneity and conflicting preferences of the participants, and to deal with important aspects such as participant proximity, group size, and duration.

A lot of research has been done under the topic of decision support systems in the last few decades, however, it is extremely important to develop new strategies that constitute better decision support. Artificial Intelligence techniques demonstrate good potential to overcome many of today’s challenges in such areas as facilitating decision-makers in configuring preferences, acquiring user profiles in a non-intrusive (implicit) and time-consuming manner, creating new strategies to deal with non-cooperative behaviors, dealing with large-scale group decision-making events, using argumentation models in order to make suggestions and recommendations more understandable and supported by evidences, dealing with cognitive and affective aspects, dealing with big data, and developing modern consensus reaching processes, among others.

The purpose of this Special Issue is to explore new Artificial Intelligence solutions to overcome the current challenges of decision support systems.

Topics that can be relevant for this Special Issue include:

  • Decision support systems
  • Artificial Intelligence Techniques and Autonomous Agents in Decision Support Systems
  • Group and Multi-Criteria Decision Analysis
  • Cognitive and Affective Aspects in Decision-Making and Recommendations (Emotions, Personality, Mood, Motivations, among others)
  • Context-Aware Recommendation
  • Argumentation, persuasion, and explanation through argumentation
  • Large Scale Group Decision-Making
  • Consensus Reaching Processes
  • Behavior change support systems
  • Interdisciplinary applications: Online trading, e-Health and Well-being, Industry, Tourism and Leisure, Assisted learning environments, Sustainability, Energy efficiency, Smart Cities and Dynamic Environments in general.

Prof. Dr. Maria Goreti Carvalho Marreiros
Dr. João Carneiro
Dr. Jaume Jordan
Guest Editors

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Published Papers (3 papers)

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Research

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13 pages, 4116 KiB  
Article
An Intelligent Coaching Prototype for Elderly Care
by Diogo Martinho, Vítor Crista, João Carneiro, Juan Manuel Corchado and Goreti Marreiros
Electronics 2022, 11(3), 460; https://doi.org/10.3390/electronics11030460 - 03 Feb 2022
Cited by 5 | Viewed by 2118
Abstract
The world ageing problem is prompting new sustainable ways to support elderly people. As such, it is important to promote personalized and intelligent ways to assure the active and healthy ageing of the population. Technological breakthroughs have led to the development of personalized [...] Read more.
The world ageing problem is prompting new sustainable ways to support elderly people. As such, it is important to promote personalized and intelligent ways to assure the active and healthy ageing of the population. Technological breakthroughs have led to the development of personalized healthcare systems, capable of monitoring and providing feedback on different aspects that can improve the health of the elderly person. Furthermore, defining motivational strategies to persuade the elderly person to be healthier and stay connected to such systems is also fundamental. In this work, a coaching system is presented, especially designed to support elderly people and motivate them to pursue healthier ways of living. To do this, a coaching application is developed using both a cognitive virtual assistant to directly interact with the elderly person and provide feedback on his/her current health condition, and several gamification techniques to motivate the elderly person to stay engaged with the application. Additionally, a set of simulations were performed to validate the proposed system in terms of the support and feedback provided to the user according to his progress, and through interactions with the cognitive assistant. Full article
(This article belongs to the Special Issue Decision Support Systems: Challenges and Solutions)
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14 pages, 22457 KiB  
Article
Artificial Immune System for Fault Detection and Classification of Semiconductor Equipment
by Hyoeun Park, Jeong Eun Choi, Dohyun Kim and Sang Jeen Hong
Electronics 2021, 10(8), 944; https://doi.org/10.3390/electronics10080944 - 15 Apr 2021
Cited by 19 | Viewed by 3635
Abstract
Semiconductor manufacturing comprises hundreds of consecutive unit processes. A single misprocess could jeopardize the whole manufacturing process. In current manufacturing environments, data monitoring of equipment condition, wafer metrology, and inspection, etc., are used to probe any anomaly during the manufacturing process that could [...] Read more.
Semiconductor manufacturing comprises hundreds of consecutive unit processes. A single misprocess could jeopardize the whole manufacturing process. In current manufacturing environments, data monitoring of equipment condition, wafer metrology, and inspection, etc., are used to probe any anomaly during the manufacturing process that could affect the final chip performance and quality. The purpose of investigation is fault detection and classification (FDC). Various methods, such as statistical or data mining methods with machine learning algorithms, have been employed for FDC. In this paper, we propose an artificial immune system (AIS), which is a biologically inspired computing algorithm, for FDC regarding semiconductor equipment. Process shifts caused by parts and modules aging over time are main processes of failure cause. We employ state variable identification (SVID) data, which contain current equipment operating condition, and optical emission spectroscopy (OES) data, which represent plasma process information obtained from faulty process scenario with intentional modification of the gas flow rate in a semiconductor fabrication process. We achieved a modeling prediction accuracy of modeling of 94.69% with selected SVID and OES and an accuracy of 93.68% with OES data alone. To conclude, the possibility of using an AIS in the field of semiconductor process decision making is proposed. Full article
(This article belongs to the Special Issue Decision Support Systems: Challenges and Solutions)
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Review

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34 pages, 2648 KiB  
Review
Intelligent Cognitive Assistants for Attitude and Behavior Change Support in Mental Health: State-of-the-Art Technical Review
by Tine Kolenik and Matjaž Gams
Electronics 2021, 10(11), 1250; https://doi.org/10.3390/electronics10111250 - 24 May 2021
Cited by 18 | Viewed by 3947
Abstract
Intelligent cognitive assistant (ICA) technology is used in various domains to emulate human behavior expressed through synchronous communication, especially written conversation. Due to their ability to use individually tailored natural language, they present a powerful vessel to support attitude and behavior change. Behavior [...] Read more.
Intelligent cognitive assistant (ICA) technology is used in various domains to emulate human behavior expressed through synchronous communication, especially written conversation. Due to their ability to use individually tailored natural language, they present a powerful vessel to support attitude and behavior change. Behavior change support systems are emerging as a crucial tool in digital mental health services, and ICAs exceed in effective support, especially for stress, anxiety and depression (SAD), where ICAs guide people’s thought processes and actions by analyzing their affective and cognitive phenomena. Currently, there is no comprehensive review of such ICAs from a technical standpoint, and existing work is conducted exclusively from a psychological or medical perspective. This technical state-of-the-art review tried to discern and systematize current technological approaches and trends as well as detail the highly interdisciplinary landscape of intersections between ICAs, attitude and behavior change, and mental health, focusing on text-based ICAs for SAD. Ten papers with systems, fitting our criteria, were selected. The systems varied significantly in their approaches, with the most successful opting for comprehensive user models, classification-based assessment, personalized intervention, and dialogue tree conversational models. Full article
(This article belongs to the Special Issue Decision Support Systems: Challenges and Solutions)
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