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Hybrid Intelligent Decision Support Using a Semiotic Case-Based Reasoning and Self-Organizing Maps.

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Title: Hybrid Intelligent Decision Support Using a Semiotic Case-Based Reasoning and Self-Organizing Maps.
Authors: Martins, Denis Mayr Lima1 (AUTHOR) dmlm@upe.poli.br, de Lima Neto, Fernando Buarque1 (AUTHOR) fbln@upe.poli.br
Source: IEEE Transactions on Systems, Man & Cybernetics. Systems. Mar2020, Vol. 50 Issue 3, p863-870. 8p.
Subjects: Case-based reasoning, Self-organizing maps, Semiotics, Statistical decision making, Decision making, Decision support systems, Cognitive science
Abstract: Human decision-making involves cognitive processes of selection, evaluation, and interpretation among candidate solutions in order to solve decision problems. Nonintelligent decision support systems (DSS) lack automatic interpretations, at least in a low level scale, which can lead to undesired solutions. To tackle this limitation, hence producing enhanced decision making, a hybrid intelligent decision support approach is presented, which combines case-based reasoning cycle, semiotic concepts, and self-organizing maps. In addition, a novel sign deconstruction mechanism is introduced as foundation of the new approach and affords better interpretability and contextualization of candidate solutions without compromising efficiency and precision. The obtained results confirm that our proposed approach has the potential to be readily applicable to decision problems, particularly the ones that are of subjective nature. Moreover, the put forward approach may integrate some unlikely elements of linguistics and cognitive science which could fundamentally help the enhancement of DSS. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Systems, Man & Cybernetics. Systems is the property of IEEE 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.)
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  Data: <searchLink fieldCode="DE" term="%22Case-based+reasoning%22">Case-based reasoning</searchLink><br /><searchLink fieldCode="DE" term="%22Self-organizing+maps%22">Self-organizing maps</searchLink><br /><searchLink fieldCode="DE" term="%22Semiotics%22">Semiotics</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+decision+making%22">Statistical decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+support+systems%22">Decision support systems</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+science%22">Cognitive science</searchLink>
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  Data: Human decision-making involves cognitive processes of selection, evaluation, and interpretation among candidate solutions in order to solve decision problems. Nonintelligent decision support systems (DSS) lack automatic interpretations, at least in a low level scale, which can lead to undesired solutions. To tackle this limitation, hence producing enhanced decision making, a hybrid intelligent decision support approach is presented, which combines case-based reasoning cycle, semiotic concepts, and self-organizing maps. In addition, a novel sign deconstruction mechanism is introduced as foundation of the new approach and affords better interpretability and contextualization of candidate solutions without compromising efficiency and precision. The obtained results confirm that our proposed approach has the potential to be readily applicable to decision problems, particularly the ones that are of subjective nature. Moreover, the put forward approach may integrate some unlikely elements of linguistics and cognitive science which could fundamentally help the enhancement of DSS. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IEEE Transactions on Systems, Man & Cybernetics. Systems is the property of IEEE 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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      – SubjectFull: Self-organizing maps
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              Text: Mar2020
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