Intelligent Problem Solving Model and its Cross Research Directions Based on Factor Space and Extenics

Xingsen Li (), Junlin Zeng (), Haitao Liu () and Peizhuang Wang ()
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Xingsen Li: Guangdong University of Technology
Junlin Zeng: Guangdong University of Technology
Haitao Liu: Liaoning Technical University
Peizhuang Wang: Liaoning Technical University

Annals of Data Science, 2022, vol. 9, issue 3, No 4, 469-484

Abstract: Abstract Artificial intelligence technology has made important progress in machine learning and problem-solving with relatively determined boundary conditions. However, the more common open problems with uncertain boundary conditions in management practice still depend on the experience mastered by individuals. The combination of Extenics, Factor Space, and knowledge management will potentially solve this kind of problem intelligently to a extent. Based on Extenics and factor space theory, this paper studies the Extension model of open problems, explores the intelligent expansion mechanism of factor knowledge in big data environment, and constructs the double integration of multi granularity factor knowledge space and expert experience knowledge. We try to make Extenics and factor space theory complement each other in the field of problem solving, reveal the knowledge expansion mechanism of open problem solving in the big data environment, provide a novel theoretical perspective and method basis for knowledge based intelligent service on factor mining. This paper will also provide theoretical research directions for building a new generation of problem-oriented new factor knowledge base, promote the deep integration of knowledge management and artificial intelligence leading to a new direction of knowledge engineering based on factor space and Extenics.

Keywords: Factor space; Extenics; Problem solving model; Data science; Intelligent knowledge management (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s40745-022-00385-w

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