AI Partial Order Planning MCQ
This section focuses on "Partial Order Planning" in Artificial Intelligence. These Multiple Choice Questions (mcq) should be practiced to improve the AI skills required for various interviews (campus interviews, walk-in interviews, company interviews), placements, entrance exams and other competitive examinations.
1. The process by which the brain incrementally orders actions needed to complete a specific task is referred as ______________
Explanation: The process by which the brain incrementally orders actions needed to complete a specific task is referred as Partial order planning.
2. Following is/are the components of the partial order planning.
Explanation: All the above option are correct.
3. Sussman Anomaly can be easily and efficiently solved by partial order planning.
Explanation: True : Sussman Anomaly can be easily and efficiently solved by partial order planning.
4. Which of the following search belongs to totally ordered plan search?
Explanation: Forward and backward state-space search are particular forms of totally ordered plan search.
5. Which cannot be taken as advantage for totally ordered plan search?
Explanation: As the search explore only linear sequences of actions, So they cannot take advantage of problem decomposition.
6. What is the advantage of totally ordered plan in constructing the plan?
Explanation: Totally ordered plan has the advantage of flexibility in the order in which it constructs the plan.
7. How many possible plans are available in partial-order solution?
Explanation: The partial-order solution corresponds to six possible total-order plans.
8. What are present in the empty plan?
Explanation: The 'empty' plan contains just the start and finish actions.
9. What are not present in start actions?
Explanation: Start has no precondition and has as its effects all the literals in the initial state of the planning problem.
10. What are not present in finish actions?
Explanation: Finish has no effects and has as its preconditions the goal literals of the planning algorithm.