Uncertainty is incomplete confidence that information, statements, or conclusions are true or precise. In intelligent-system reasoning, it arises when inputs are inaccurate, ambiguous, incomplete, or insufficient for a definitive conclusion. section 2.4 section 2.5.3
Reasoning systems address uncertainty in complementary ways:
- Deductive reasoning requires correct premises and explicit rules, so uncertain or missing information can block a logical conclusion. section 2.4
- Inductive reasoning generalizes from labeled examples, enabling predictions for unseen cases rather than only memorized matches. section 2.4
- LLMs use probabilistic reasoning to weigh possibilities and recommend priorities when information is ambiguous or incomplete. section 2.5.3
Ambiguous medical symptoms
A patient’s nonspecific symptoms and incomplete records may support several diagnoses. An LLM can evaluate multiple possibilities and suggest a prioritized diagnostic approach instead of asserting one definitive answer. section 2.5.3
