Tacit Knowledge Extraction via Logic Augmented Generation and Active Inference

📰 ArXiv cs.AI

Extract tacit knowledge using logic augmented generation and active inference to improve machine-interpretable forms of human expertise

advanced Published 11 May 2026

Action Steps

Apply logic augmented generation to identify implicit assumptions in procedural domains
Use active inference to model contextual constraints and embodied skills
Configure knowledge engineering pipelines to incorporate tacit knowledge extraction
Test the effectiveness of tacit knowledge extraction in improving machine performance
Compare the results of logic augmented generation and active inference with traditional knowledge engineering methods

Who Needs to Know This

Researchers and engineers working on knowledge engineering and artificial intelligence can benefit from this approach to capture and formalize tacit knowledge

Key Insight

💡 Tacit knowledge can be extracted and formalized using logic augmented generation and active inference to improve machine-interpretable forms of human expertise

Key Takeaways

Extract tacit knowledge using logic augmented generation and active inference to improve machine-interpretable forms of human expertise

Full Article

Title: Tacit Knowledge Extraction via Logic Augmented Generation and Active Inference

Abstract:
arXiv:2605.07639v1 Announce Type: new Abstract: Tacit knowledge plays a central role in human expertise, yet it remains difficult to capture, formalize, and reuse in machine-interpretable form. This challenge is especially relevant in procedural domains, where successful execution depends not only on explicit instructions, but also on implicit assumptions, contextual constraints, embodied skills, and experience-based judgments rarely documented. As a result, current knowledge engineering pipelin

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