A knowledge-driven causal evolution framework based on extenics element theory for intelligent problem analysis

Jianan Yao

doi:10.1016/j.eswa.2026.133090

A knowledge-driven causal evolution framework based on extenics element theory for intelligent problem analysis

2026-09-29

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DOI：10.1016/j.eswa.2026.133090

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Understanding and tracing the root causes of engineering problems is essential for improving system reliability and enabling intelligent decision-making. Traditional causal chain analysis based on the Theory of Inventive Problem Solving provides a qualitative pathway for identifying problem origins; however, it often suffers from incomplete exploration of lower-level causes and lacks quantitative criteria for determining key problems. To overcome these limitations, this study proposes a knowledge-driven causal evolution framework that integrates the basic-element theory of Extenics. The framework represents problems in a unified structure of matter-elements, affair-elements, and relation-elements, allowing hierarchical decomposition and causal propagation through their logical correlations. By quantifying the relationships among basic elements, the proposed framework evaluates the causal significance of each problem node and identifies the most critical factors affecting system performance. A case study on improving the controllability of flexible bodies demonstrates that this approach enables more comprehensive, systematic, and quantitative causal reasoning. The proposed framework not only enhances the analytical depth of causal chain analysis but also provides a theoretical foundation for intelligent problem and-driven evolution.

Keywords:

Causal chain analysis

Extenics

Basic-Element theory

Modular robot

Quantitative evaluation

Innovation methodology

A knowledge-driven causal evolution framework based on extenics element theory for intelligent problem analysis

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