Designing arithmetic neural primitive for sub-symbolic aggregation of linguistic assessments. (December 2020)

• Record Type: Journal Article
• Authors: Demidovskij, Alexander; Babkin, Eduard
• Is Part Of: Journal of physics. Volume 1680(2020)
• Publication Date: 2020-12
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1680/1/012007
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: The very first step towards a challenging goal of creation of monolithic generic neuro-symbolic systems is application of sub-symbolic ideas to particular symbolic algorithms like aggregation of fuzzy linguistic assessments during Linguistic Decision Making. A novel theoretical idea is to express this aggregation as structural manipulations and translate them in a neuroalgorithm. Tensor Product Representation (TPR) methodology provides a generic framework of designing neural networks that do not require training and produce an exact result equivalent to the result of symbolic algorithms. This paper demonstrates design of TPR-based arithmetic as a basic building block for expressing linguistic assessments aggregation on a sub-symbolic level and a neural architecture for the basic arithmetic operation.