AI Partial Order Planning MCQ
Partial Order Planning MCQs : 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.