Artificial Intelligence MCQs

Page No. 23

Greedy search strategy chooses the node for expansion in ___________


a Shallowest


bDeepest


cThe one closest to the goal node


dMinimum heuristic cost



Heuristic function h(n) is ________


aLowest path cost


bCheapest path from root to goal node


cEstimated cost of cheapest path from root to goal node


d Average path cost



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.


aTrue


bFalse


cA & B


dNone of these


View Answer True

Best-First search can be implemented using the following data structure.


aQueue


bStack


cPriority Queue


dCircular Queue


View Answer Priority Queue

Best-First search is a type of informed search, which uses ________________ to choose the best next node for expansion.


aEvaluation function returning lowest evaluation


b Evaluation function returning highest evaluation


cEvaluation function returning lowest & highest evaluation


d None of them is applicable



Uninformed search strategies are better than informed search strategies.


a True


bFalse


cA & B


dNone of these


View Answer True

The search strategy the uses a problem specific knowledge is known as ___________


aInformed Search


bBest First Search


cHeuristic Search


dAll of the mentioned


View Answer All of the mentioned

A* algorithm is based on ___________


aBreadth-First-Search


bDepth-First –Search


cBest-First-Search


dHill climbing


View Answer Best-First-Search

Searching using query on Internet is, use of ___________ type of agent.


aOffline agent


bOnline agent


cBoth Offline & Online agent


d Goal Based & Online agent



What are the two main features of Genetic Algorithm?


a Fitness function & Crossover techniques


b Crossover techniques & Random mutation


c Individuals among the population & Random mutation


dRandom mutation & Fitness function



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