Agents may need to handle uncertainty, whether due to partial observability, no determinism, or a combination of the two. An agent may never know for certain what state it’s in or where it will end up after a sequence of actions.

Uncertain data: When data can be categorized as missing, unreliable, ambiguous, inconsistent, subjective, noisy, data derived from defaults, the data represents a guess by expert; it can be called as uncertain data.

Uncertain knowledge: When available knowledge has multiple causes leading to multiple effects or incomplete knowledge of causality in the domain which can’t be defined in advanced, such knowledge is called as uncertain knowledge.

Uncertain knowledge representation: The representations which provides a restricted model of the real system, limited expressiveness of representation mechanism and data with imprecise representation; is called as uncertain knowledge representation.

Let’s consider an example of uncertain reasoning: diagnosing a dental patient’s toothache.

Diagnosis—whether for medicine, automobile repair, or whatever—almost always involves uncertainty. Let us try to write rules for dental diagnosis using propositional logic, so that we can see how the logical approach breaks down.

Consider the following simple rule:

Toothache ⇒Cavity.

The problem is that this rule is wrong. Not all patients with toothaches have cavities; some of them have gum disease, an abscess, or one of several other problems:

Toothache ⇒Cavity ∨GumProblem∨Abscess.

Unfortunately, in order to make the rule true, we have to add an almost unlimited list of possible problems. We could try turning the rule into causal rule:

Cavity ⇒Toothache.

But this rule is not right either; not all cavities cause pain. The only way to fix the rule is to make it logically exhaustive: to augment the left-hand side with all the qualifications required for a cavity to cause a toothache.