This set of Artificial Intelligence Multiple Choice Questions & Answers (MCQs) focuses on “Partial Order Planning”.
1. The process by which the brain incrementally orders actions needed to complete a specific task is referred as,
a) Planning problem
b) Partial order planning
c) Total order planning
d) Both Planning problem & Partial order planning
Explanation: Definition of partial order planning.
2. To complete any task, the brain needs to plan out the sequence by which to execute the behavior. One way the brain does this is with a partial-order plan. State whether true or false.
3. In partial order plan.
A. Relationships between the actions of the behavior are set prior to the actions
B. Relationships between the actions of the behavior are not set until absolutely necessary
Choose the correct option.
a) A is true
b) B is true
c) Either A or B can be true depending upon situation
d) Neither A nor B is true
Explanation: Relationship between behavior and actions is established dynamically.
4. Partial-order planning exhibits the Principle of Least Commitment, which contributes to the efficiency of this planning system as a whole.
5. Following is/are the components of the partial order planning.
c) Causal Links
d) All of the mentioned
Explanation: Bindings: The bindings of the algorithm are the connections between specific variables in the action. Bindings, as ordering, only occur when it is absolutely necessary.
Causal Links: Causal links in the algorithm are those that categorically order actions. They are not the specific order (1,2,3) of the actions, rather the general order as in Action 2 must come somewhere after Action 1, but before Action 2.
Plan Space: The plan space of the algorithm is constrained between its start and finish. The algorithm starts, producing the initial state and finishes when all parts of the goal is been achieved.
6. Partial-order planning is the opposite of total-order planning.
Explanation: Partial-order planning is the opposite of total-order planning, in which actions are sequenced all at once and for the entirety of the task at hand.
7. Sussman Anomaly can be easily and efficiently solved by partial order planning.
8. Sussman Anomaly illustrates a weakness of interleaved planning algorithm.
Explanation: Sussman Anomaly illustrates a weakness of noninterleaved planning algorithm.
9. One the main drawback of this type of planning system is that it requires a lot of computational powers at each node.
10. What are you predicating by the logic: ۷x: €y: loyalto(x, y).
a) Everyone is loyal to some one
b) Everyone is loyal to all
c) Everyone is not loyal to someone
d) Everyone is loyal
Explanation: ۷x denotes Everyone or all, and €y someone and loyal to is the proposition logic making map x to y.
11. A plan that describe how to take actions in levels of increasing refinement and specificity is
a) Problem solving
c) Non-hierarchical plan
d) Hierarchical plan
Explanation: A plan that describes how to take actions in levels of increasing refinement and specificity is Hierarchical (e.g., “Do something” becomes the more specific “Go to work,” “Do work,” “Go home.”) Most plans are hierarchical in nature.
12. A constructive approach in which no commitment is made unless it is necessary to do so, is
a) Least commitment approach
b) Most commitment approach
c) Nonlinear planning
d) Opportunistic planning
Explanation: Because we are not sure about the outcome.
13. Uncertainty arises in the Wumpus world because the agent’s sensors give only
a) Full & Global information
b) Partial & Global Information
c) Partial & local Information
d) Full & local information
Explanation: The Wumpus world is a grid of squares surrounded by walls, where each square can contain agents and objects. The agent (you) always starts in the lower left corner, a square that will be labeled [1, 1]. The agent’s task is to find the gold, return to [1, 1] and climb out of the cave. Therefore, uncertainty is there as the agent gives partial and local information only. Global variable are not goal specific problem solving.
Sanfoundry Global Education & Learning Series – Artificial Intelligence.