, ACTING UNDER UNCERTAINTY
● Agents may need to handle uncertainty, whether due to partial observability,
nondeterminism, 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.
The approach has significant drawbacks when taken literally as a recipe for
creating agent programs:
● When interpreting partial sensor information, a logical agent must consider
every logically possible explanation for the observations, no matter how
unlikely. This leads to impossible large and complex belief-state
representations.
● A correct contingent plan that handles every eventuality can grow arbitrarily
large and must consider arbitrarily unlikely contingencies.
● Sometimes there is no plan that is guaranteed to achieve the goal—yet the
agent must act. It must have some way to compare the merits of plans that
are not guaranteed.
, The right thing to do—the rational decision—therefore depends on both the
relative importance of various goals and the likelihood that, and degree to which,
they will be achieved.
An example of uncertain reasoning: diagnosing a dental patient’s toothache.
Let us try to write rules for dental diagnosis using propositional logic
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 . . .
Then we write add an almost unlimited list of possible problems.
Cavity ⇒ Toothache .
● Agents may need to handle uncertainty, whether due to partial observability,
nondeterminism, 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.
The approach has significant drawbacks when taken literally as a recipe for
creating agent programs:
● When interpreting partial sensor information, a logical agent must consider
every logically possible explanation for the observations, no matter how
unlikely. This leads to impossible large and complex belief-state
representations.
● A correct contingent plan that handles every eventuality can grow arbitrarily
large and must consider arbitrarily unlikely contingencies.
● Sometimes there is no plan that is guaranteed to achieve the goal—yet the
agent must act. It must have some way to compare the merits of plans that
are not guaranteed.
, The right thing to do—the rational decision—therefore depends on both the
relative importance of various goals and the likelihood that, and degree to which,
they will be achieved.
An example of uncertain reasoning: diagnosing a dental patient’s toothache.
Let us try to write rules for dental diagnosis using propositional logic
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 . . .
Then we write add an almost unlimited list of possible problems.
Cavity ⇒ Toothache .
