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In A* 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
In A* 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
A* is optimal if h(n) is an admissible heuristic-that is, provided that h(n) never underestimates the cost to reach the goal. a) True b) False
What is the other name of informed search strategy? a) Simple search b) Heuristic search c) Online search d) None of the mentioned
What is the heuristic function of greedy best-first search? a) f(n) != h(n) b) f(n) < h(n) c) f(n) = h(n) d) f(n) > h(n)
Which search uses only the linear space for searching? a) Best-first search b) Recursive best-first search c) Depth-first search d) None of the mentioned
Which method is used to search better by learning? a) Best-first search b) Depth-first search c) Metalevel state space d) None of the mentioned
Which is used to improve the performance of heuristic search? a) Quality of nodes b) Quality of heuristic function c) Simple form of nodes d) None of the mentioned
In many problems the path to goal is irrelevant, this class of problems can be solved using, a) Informed Search Techniques b) Uninformed Search Techniques c) Local Search Techniques d) Only a and b
Though local search algorithms are not systematic, key advantages would include a) Less memory b) More time c) Finds a solution in large infinite space d) No optimum solution
A complete, local search algorithm always finds goal if one exists, an optimal algorithm always finds a global minimum/maximum. State whether True or False. a) True b) False
Hill-Climbing algorithm terminates when, a) Stopping criterion met b) Global Min/Max is achieved c) No neighbor has higher value d) Local Min/Max is achieved
One of the main cons of hill-climbing search is, a) Terminates at local optimum b) Terminates at global optimum c) Does not find optimum solution d) Fail to find a solution
Stochastic hill climbing chooses at random from among the uphill moves; the probability of selection can vary with the steepness of the uphil1 move. a) True b) False
Hill-Climbing approach stuck for the following reasons a) Local maxima b) Ridges c) Plateaux d) All of above
