Special Issue "Artificial Intelligence and Decision Support Systems"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 1 August 2020.

Special Issue Editor

Dr. Wojciech Sałabun
E-Mail Website
Guest Editor
Department of Artificial Intelligence method and Applied Mathematics, Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Szczecin, ul. Żołnierska 49, 71-210 Szczecin, Poland
Interests: decision support system; decision making; MCDA; fuzzy logic; artificial intelligence

Special Issue Information

Dear Colleagues,

Intelligent decision support systems are used to make better reliable decisions. However, it is a tough challenge because it requires the engagement of many opposing criteria. In addition to classical multi-criteria decision-making (MCDM) methods, artificial intelligence (AI) methods are also used. The modern approach involves using artificial intelligence methods for handling uncertainty data.

In both individual and group decision-making, attention must be paid to the complexity of the decision-making problem itself, and, thus, to the procedure complexity that is designed to select the best decisions from a set of candidate solutions or to establish a ranking. Moreover, new methods and modifications of existing ones are realized to utilize uncertain data to obtain reliable rankings effectively.

Therefore, the purpose of this Special Issue is to present the latest developments in artificial intelligence, MCDM methods, and new algorithms for decision support systems. Investigators in the field are invited to contribute their original, unpublished theoretical, and applied works. Both research and review papers are welcome.

Topics of interest include but are not limited to:

Artificial intelligence methods and applications:

  • Fuzzy sets
  • Interval arithmetic
  • Intuistionic fuzzy sets
  • Hesitant fuzzy sets
  • Machine learning
  • Deep learning

Decision support systems:

  • New MCDM methods
  • Selection problem
  • Sustainable development
  • Uncertain decision problems
  • Imprecise decision problems
  • PROMETHEE
  • AHP/ANP
  • TOPSIS
  • ELECTRE

Dr. Wojciech Sałabun
Guest Editor

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 papers will be 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. Information is an international peer-reviewed open access monthly 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 1000 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.

Published Papers (3 papers)

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Research

Open AccessArticle
Linguistic Pythagorean Einstein Operators and Their Application to Decision Making
Information 2020, 11(1), 46; https://doi.org/10.3390/info11010046 - 16 Jan 2020
Abstract
Linguistic Pythagorean fuzzy (LPF) set is an efficacious technique to comprehensively represent uncertain assessment information by combining the Pythagorean fuzzy numbers and linguistic variables. In this paper, we define several novel essential operations of LPF numbers based upon Einstein operations and discuss several [...] Read more.
Linguistic Pythagorean fuzzy (LPF) set is an efficacious technique to comprehensively represent uncertain assessment information by combining the Pythagorean fuzzy numbers and linguistic variables. In this paper, we define several novel essential operations of LPF numbers based upon Einstein operations and discuss several relations between these operations. For solving the LPF numbers fusion problem, several LPF aggregation operators, including LPF Einstein weighted averaging (LPFEWA) operator, LPF Einstein weighted geometric (LPFEWG) operator and LPF Einstein hybrid operator, are propounded; the prominent characteristics of these operators are investigated as well. Furthermore, a multi-attribute group decision making (MAGDM) approach is presented on the basis of the developed operators under an LPF environment. Ultimately, two application cases are utilized to demonstrate the practicality and feasibility of the developed decision approach and the comparison analysis is provided to manifest the merits of it. Full article
(This article belongs to the Special Issue Artificial Intelligence and Decision Support Systems)
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Open AccessArticle
Artificial Intelligence-Enhanced Decision Support for Informing Global Sustainable Development: A Human-Centric AI-Thinking Approach
Information 2020, 11(1), 39; https://doi.org/10.3390/info11010039 - 11 Jan 2020
Abstract
Sustainable development is crucial to humanity. Utilization of primary socio-environmental data for analysis is essential for informing decision making by policy makers about sustainability in development. Artificial intelligence (AI)-based approaches are useful for analyzing data. However, it was not easy for people who [...] Read more.
Sustainable development is crucial to humanity. Utilization of primary socio-environmental data for analysis is essential for informing decision making by policy makers about sustainability in development. Artificial intelligence (AI)-based approaches are useful for analyzing data. However, it was not easy for people who are not trained in computer science to use AI. The significance and novelty of this paper is that it shows how the use of AI can be democratized via a user-friendly human-centric probabilistic reasoning approach. Using this approach, analysts who are not computer scientists can also use AI to analyze sustainability-related EPI data. Further, this human-centric probabilistic reasoning approach can also be used as cognitive scaffolding to educe AI-Thinking in the analysts to ask more questions and provide decision making support to inform policy making in sustainable development. This paper uses the 2018 Environmental Performance Index (EPI) data from 180 countries which includes performance indicators covering environmental health and ecosystem vitality. AI-based predictive modeling techniques are applied on 2018 EPI data to reveal the hidden tensions between the two fundamental dimensions of sustainable development: (1) environmental health; which improves with economic growth and increasing affluence; and (2) ecosystem vitality, which worsens due to industrialization and urbanization. Full article
(This article belongs to the Special Issue Artificial Intelligence and Decision Support Systems)
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Open AccessArticle
Complex q-Rung Orthopair Fuzzy Aggregation Operators and Their Applications in Multi-Attribute Group Decision Making
Information 2020, 11(1), 5; https://doi.org/10.3390/info11010005 - 20 Dec 2019
Abstract
In this manuscript, the notions of q-rung orthopair fuzzy sets (q-ROFSs) and complex fuzzy sets (CFSs) are combined is to propose the complex q-rung orthopair fuzzy sets (Cq-ROFSs) and their fundamental laws. The Cq-ROFSs are an important way to express uncertain information, and [...] Read more.
In this manuscript, the notions of q-rung orthopair fuzzy sets (q-ROFSs) and complex fuzzy sets (CFSs) are combined is to propose the complex q-rung orthopair fuzzy sets (Cq-ROFSs) and their fundamental laws. The Cq-ROFSs are an important way to express uncertain information, and they are superior to the complex intuitionistic fuzzy sets and the complex Pythagorean fuzzy sets. Their eminent characteristic is that the sum of the qth power of the real part (similarly for imaginary part) of complex-valued membership degree and the qth power of the real part (similarly for imaginary part) of complex-valued non‐membership degree is equal to or less than 1, so the space of uncertain information they can describe is broader. Under these environments, we develop the score function, accuracy function and comparison method for two Cq-ROFNs. Based on Cq-ROFSs, some new aggregation operators are called complex q-rung orthopair fuzzy weighted averaging (Cq-ROFWA) and complex q-rung orthopair fuzzy weighted geometric (Cq-ROFWG) operators are investigated, and their properties are described. Further, based on proposed operators, we present a new method to deal with the multi‐attribute group decision making (MAGDM) problems under the environment of fuzzy set theory. Finally, we use some practical examples to illustrate the validity and superiority of the proposed method by comparing with other existing methods. Full article
(This article belongs to the Special Issue Artificial Intelligence and Decision Support Systems)
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