AGL: actionable granular logic for verifiable specifications and reasoningin AI decision–action systems
Andrzej Jankowski
a:1:{s:5:"en_US";s:3:"UWM";}Abstract
Decision–action systems deployed in high-stakes domains require auditability and bounded (profiled) verifiabil-ity of the reasoning core, which cannot be ensured by empirical safeguards alone. We introduce AGL (Actionable Granular Logic) as a formal framework that combines an auditable knowledge-state layer (MT-FOGL) with work-flows described by a regular-program syntax in the style of FO-PDL. Vague and probabilistic assessments are encapsulated as Information Granules and exposed to the core solely via threshold atoms, thereby keeping the rule/procedure interface within classical two-valued logic. To control verifiability, we restrict quantification and program tests to guarded profiles GF/RGF, preserving decidability with known worst-case complexity bounds. Complex estimation mechanisms are deliberately kept outside the verifiable core (the Decidability Split). The approach is illustrated using non-normative decision patterns in a medical context, intentionally independent of any particular clinical guideline version.
Keywords:
Actionable Granular Logic, LLM Hallucinations, Guarded Fragments, Neuro-symbolic AI, Clinical Decision Support, Verifiable AI, OnkoBotReferences
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