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A semiotic-inspired machine for personalized multi-criteria intelligent decision support.

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Title: A semiotic-inspired machine for personalized multi-criteria intelligent decision support.
Authors: de Lima Neto, Fernando Buarque1 fbln@ecomp.poli.br, Lima Martins, Denis Mayr1,2 denis.martins@wi.uni-muenster.de, Vossen, Gottfried2,3 vossen@wi.uni-muenster.de
Source: Data & Knowledge Engineering. Sep2018, Vol. 117, p225-238. 14p.
Subjects: Semiotics, Decision support systems, Computational intelligence, Algorithms, Decision making
Abstract: Abstract The need for appropriate decisions to tackle complex problems increases every day. Selecting destinations for vacation, comparing and optimizing resources to create valuable products, or purchasing a suitable car are just a few examples of puzzling situations in which there is no standard form to find an appropriate solution. Such scenarios become arduous when the number of possibilities, restrictions, and factors affecting the decision rise, thereby turning decision makers into almost mere spectators. In such circumstances, decision support systems (DSS) can play an important role in guiding people and organizations towards more accurate decision making. However, conventional DSS lack the necessary adaptability to account for dynamic changes and are frequently inadequate to tackle the subjectivity inherent in decision-maker's preferences and intention. We argue that these shortcomings can be addressed by a suitable combination of Semiotic Theory and Computational Intelligence algorithms, which together can make up a new generation of DSS. In this article, a formal description of an Intelligent Semiotic Machine is provided and tried out in practical decision contexts. The results obtained show that our approach can provide well-suited decisions based on user preferences, achieving appropriateness while fanning out subjective options without losing decision context, objectivity, or accuracy. [ABSTRACT FROM AUTHOR]
Copyright of Data & Knowledge Engineering is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: <searchLink fieldCode="DE" term="%22Semiotics%22">Semiotics</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+support+systems%22">Decision support systems</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+intelligence%22">Computational intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink>
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  Data: Abstract The need for appropriate decisions to tackle complex problems increases every day. Selecting destinations for vacation, comparing and optimizing resources to create valuable products, or purchasing a suitable car are just a few examples of puzzling situations in which there is no standard form to find an appropriate solution. Such scenarios become arduous when the number of possibilities, restrictions, and factors affecting the decision rise, thereby turning decision makers into almost mere spectators. In such circumstances, decision support systems (DSS) can play an important role in guiding people and organizations towards more accurate decision making. However, conventional DSS lack the necessary adaptability to account for dynamic changes and are frequently inadequate to tackle the subjectivity inherent in decision-maker's preferences and intention. We argue that these shortcomings can be addressed by a suitable combination of Semiotic Theory and Computational Intelligence algorithms, which together can make up a new generation of DSS. In this article, a formal description of an Intelligent Semiotic Machine is provided and tried out in practical decision contexts. The results obtained show that our approach can provide well-suited decisions based on user preferences, achieving appropriateness while fanning out subjective options without losing decision context, objectivity, or accuracy. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Data & Knowledge Engineering is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.datak.2018.07.012
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        Text: English
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        PageCount: 14
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      – SubjectFull: Computational intelligence
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              M: 09
              Text: Sep2018
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