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Design of a Computable Approximate Reasoning Logic System for AI

Author

Listed:
  • Kaidi Liu

    (Institute of Uncertainty Information, Hebei University of Engineering, Handan 056038, China)

  • Yancang Li

    (Institute of Uncertainty Information, Hebei University of Engineering, Handan 056038, China)

  • Rong Cui

    (Institute of Uncertainty Information, Hebei University of Engineering, Handan 056038, China)

Abstract

The fuzzy logic reasoning based on the “If... then...” rule is not the inaccurate reasoning of AI against ambiguity because fuzzy reasoning is antilogical. In order to solve this problem, a redundancy theory for discriminative weight filtering containing six theorems and one M(1,2,3) model was proposed and the approximate reasoning process was shown, the system logic of AI handling ambiguity as an extension of the classical logic system was proposed. The system is a generalized dynamic logic system characterized by machine learning, which is the practical-application logic system of AI, and can effectively deal with practical problems including conflict, noise, emergencies and various unknown uncertainties. It is characterized by combining approximate reasoning and computing for specific data conversion through machine learning. Its core is data and calculations and the condition is “sufficient” high-quality training data. The innovation is that we proposed a discriminative weight filtering redundancy theory and designed a computable approximate reasoning logic system that combines approximate reasoning and calculation through machine learning to convert specific data. It is a general logic system for AI to deal with uncertainty. The study has significance in theory and practice for AI and logical reasoning research.

Suggested Citation

  • Kaidi Liu & Yancang Li & Rong Cui, 2022. "Design of a Computable Approximate Reasoning Logic System for AI," Mathematics, MDPI, vol. 10(9), pages 1-16, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1447-:d:802080
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    References listed on IDEAS

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    1. Tan Yigitcanlar & Federico Cugurullo, 2020. "The Sustainability of Artificial Intelligence: An Urbanistic Viewpoint from the Lens of Smart and Sustainable Cities," Sustainability, MDPI, vol. 12(20), pages 1-24, October.
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