1. A heuristic is a way of trying
a) To discover something or an idea embedded in a program
b) To search and measure how far a node in a search tree seems to be from a goal
c) To compare two nodes in a search tree to see if one is better than another
d) Only a) and b)
e) Only a), b) and c)
Explanation: In a heuristic approach, we discover certain idea and use heuristic functions to search for a goal and predicates to compare nodes.
2. A* algorithm is based on
b) Depth-First –Search
d) Hill climbing
Explanation: Best-first-search is giving the idea of optimization and quick choose of path, and all these characteristic lies in A* algorithm.
3. The search strategy the uses a problem specific knowledge is known as
a) Informed Search
b) Uniform-Cost Search
c) Heuristic Search
d) Best First Search
Explanation: The problem specific knowledge is also known as Heuristics and Best-First search uses some heuristic to choose the best node for expansion.
4. Uninformed search strategies are better than informed search strategies.
Explanation: Informed search strategies uses some problem specific knowledge, hence more efficient to finding goals.
5. Best-First search is a type of informed search, which uses ________________ to choose the best next node for expansion.
a) Evaluation function returning lowest evaluation
b) Evaluation function returning highest evaluation
c) Both a & b can be used
d) None of them is applicable
Explanation: Best-first search is an instance of the general TREE-SEARCH or GRAPH-SEARCH algorithm in which a node is selected for expansion based on an evaluation function, f (n) . Traditionally, the node with the lowest evaluation is selected for expansion, because the evaluation measures distance to the goal.
6. Best-First search can be implemented using the following data structure.
c) Priority Queue
d) Circular Queue
Explanation: Best-first search can be implemented within our general search framework via a priority queue, a data structure that will maintain the fringe in ascending order of f-values.
7. The name “best-first search” is a venerable but inaccurate one. After all, if we could really expand the best node first, it would not be a search at all; it would be a straight march to the goal. All we can do is choose the node that appears to be best according to the evaluation function. State whether true or false.
Explanation: If the evaluation function is exactly accurate, then this will indeed be the best node; in reality, the evaluation function will sometimes be off, and can lead the search astray.
8. Heuristic function h(n) is,
a) Lowest path cost
b) Cheapest path from root to goal node
c) Estimated cost of cheapest path from root to goal node
d) Average path cost
Explanation: Heuristic is an estimated cost.
9. Greedy search strategy chooses the node for expansion
c) The one closest to the goal node
d) Minimum heuristic cost
Explanation: Sometimes minimum heuristics can be used, sometimes maximum heuristics function can be used. It depends upon the application on which the algorithm is applied.
10. In greedy approach evaluation function is
a) Heuristic function
b) Path cost from start node to current node
c) Path cost from start node to current node + Heuristic cost
d) Average of Path cost from start node to current node and Heuristic cost
Explanation: Greedy best-first search3 tries to expand the node that is closest to the goal, on the grounds that this is likely to lead to a solution quickly. Thus, it evaluates nodes by using just the heuristic function: f (n) = h(n).
Sanfoundry Global Education & Learning Series – Artificial Intelligence.