Planning in AI involves choosing a sequence of tasks to achieve a goal. Planning in non-deterministic domains must account for uncertainty, with approaches including sensorless planning for environments with no observations, contingent planning for partially observable non-deterministic environments, and online replanning for unknown environments. An example involves painting a chair and table the same unknown color from two cans of unknown paint, requiring reasoning about percepts obtained during plan execution to solve this partially observable problem.

1. Artificial
Intelligence
Topic – Planning and
Acting in Non
Deterministic Domain
Name : Prathmesh V. Pawar
Class : T.Y. Comp
Roll No : 194

2. What is Planning in AI ?
 Planning in A.I. is about decision-
making actions performed by robots or
computer programs to achieve a
specific goal.
 Execution of the plan is about choosing
a sequence of tasks with a high
probability of accomplishing a specific
task.

3.  Planning in Non-Deterministic Domains
Consider partially observable, nondeterministic, and unknown
environments.
1. Sensor less planning (also known as conformant planning) for
environments with no observations;
2. Contingent planning for partially observable and
nondeterministic environments;
3. Online planning and Re-planning for unknown
environments.
The belief state in all these cases is uncertain. For this reason we need to change
the representation of states.

4. An Example
Given a chair and a table, the goal is to have them the same color. In the initial state
we have two cans of paint, but the colors of the paint and the chair are unknown.
Only the table is initially in the agent’s field of view.
Init : Object(Table) ∧ Object(Chair ) ∧ Can (C1) ∧ Can(C2) ∧ InView (Table)
Goal : Color(Chair, c) ∧ Color(Table, c) c is a variable

5. There are two actions:
1) Removing the lid from a paint can
2) painting an object using the paint from an open can.
Action(RemoveLid(can),
PRECOND:Can(can)
EFFECT:Open(can))
Action(Paint(x , can),
PRECOND:Object(x) ∧ Can(can) ∧ Color (can, c) ∧ Open(can)
EFFECT:Color (x , c))

6. In the fully observable case, this is not allowed.
we would have to name the action Color(x, can, c).
But in the partially observable case, we might or might not know
what color is in the can.
To solve a partially observable problem, the agent will have to
reason about the percepts it will obtain when it is executing the
plan.
The percept will be supplied by the agent’s sensors when it is
actually acting, but when it is planning it will need a model of its
sensors.