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Neural Multigranular 2-tuple Average Operator in Neural-Symbolic Decision Support Systems

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Proceedings of the Fifth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’21) (IITI 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 330))

Abstract

Construction of integrated neural-symbolic systems is an actual and challenging task. Such hybrid systems combine advantages of connectionist and symbolic approaches. In particular, neural-symbolic systems are characterized by robust learning and distributed neural computations. At the same time, they can be interpreted, described and analyzed in logical terms. Especially high interpretability is important for Decision Support Systems that operate with symbolic structures describing the problem situation, stakeholders, assessment criteria and where the reasoning process should be transparent for the Decision Maker. Such requirements bring the task of designing integrated neural-symbolic Decision Support Systems to the higher level of complexity. This paper examines underlying algorithms of a specific part of Decision Support Systems that is aggregation of experts’ assessments to make the choice among alternative solutions of the given problem. Such fuzzy and uncertain assessments are often represented as 2-tuple model or its derivatives and algorithms that aggregate them are often called operator. We demonstrate that the simple aggregation operator can be expressed in a fully connectionist form with the help of Neural Turing Machine architecture. This result sheds new light on the way principles of symbolic computation can be implemented by connectionist mechanisms.

The reported study was funded by RFBR, project number 19–37-90058.

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Authors and Affiliations

  1. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Alexander Demidovskij & Eduard Babkin

Authors
  1. Alexander Demidovskij
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  2. Eduard Babkin
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Correspondence to Alexander Demidovskij .

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Editors and Affiliations

  1. Rostovskogo Strelkovogo Polka Narodnogo, Rostov State Transport University, Rostov-on-Don, Russia

    Sergey Kovalev

  2. Bauman Moscow State Technical University, Moscow, Russia

    Valery Tarassov

  3. Department of Computer Science, VŠB-TUO, Ostrava-Poruba, Czech Republic

    Vaclav Snasel

  4. Apt. 8, Rostov State Transport University, Rostov-on-Don, Russia

    Andrey Sukhanov

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Demidovskij, A., Babkin, E. (2022). Neural Multigranular 2-tuple Average Operator in Neural-Symbolic Decision Support Systems. In: Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds) Proceedings of the Fifth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’21). IITI 2021. Lecture Notes in Networks and Systems, vol 330. Springer, Cham. https://doi.org/10.1007/978-3-030-87178-9_35

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