An agent may not know whether a claim is true or an action will work. Representing uncertainty enables verification, cautious routing, and useful questions instead of confident guessing.

Uncertainty Is Information

A Bayesian network factors a joint probability using conditional dependencies in a directed acyclic graph. Evidence updates beliefs, but the result inherits model structure and probability assumptions.

Bayesian Networks Update Structured Uncertainty

A closed loop measures an outcome, compares it with a target, and changes the next action. Without a trustworthy measure, repeated self-critique may only reinforce the same error.

Closed-Loop Control Uses Measured Feedback

A heuristic estimates which states seem promising so search spends less effort. It can improve speed but may bias exploration or sacrifice optimality unless its properties are known.

Heuristics Guide Search Without Proving the Answer

In this situation—answering when two public sources disagree about an event date—which choice best applies “Uncertainty Is Information”?

Retrieval finds candidate information relevant to a need. The returned items still require provenance, access checks, interpretation, and sometimes contradiction handling.

Retrieval Selects Evidence, Not Truth

The environment includes people, services, rules, and changing conditions outside the agent. A graph must distinguish its internal state from external facts that require observation.

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