July 14, 2025

Modeling Knowledge and Decision-Making with the Conditional Reasoning Framework

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The conditional reasoning framework addresses knowledge representation in AI, incorporating normative constraints and contextual reasoning.

The conditional reasoning framework improves knowledge representation in artificial intelligence by addressing limitations of existing models.
By introducing the orthogonal knowledge graph, the framework enables context-dependent reasoning and integrates diverse knowledge types.
It employs expectation calculus to manage uncertainty and incorporates normative reasoning principles for ethical decision-making.
The underlying Theory of Minimal Axiom Systems emphasizes the importance of axiom orthogonality in creating meaningful representations.

Wilfrido Moreno (2025) studied this question.

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The conditional reasoning framework addresses knowledge representation in AI, incorporating normative constraints and contextual reasoning.