Hill climbing sometimes called ____________ because it grabs a good neighbor state without thinking ahead about where to go next. a) Needy local search b) Heuristic local search c) Greedy local search d) Optimal local search

Hill climbing sometimes called ____________ because it grabs a good neighbor state without thinking

ahead about where to go next.

a) Needy local search

b) Heuristic local search

c) Greedy local search

d) Optimal local search
by

1 Answer

Answer :

c) Greedy local search
by

Related questions

General algorithm applied on game tree for making decision of win/lose is ____________ a) DFS/BFS Search Algorithms b) Heuristic Search Algorithms c) Greedy Search Algorithms d) MIN/MAX Algorithms

Description : General algorithm applied on game tree for making decision of win/lose is ____________ a) DFS/BFS Search Algorithms b) Heuristic Search Algorithms c) Greedy Search Algorithms d) MIN/MAX Algorithms

Last Answer : d) MIN/MAX Algorithms

Constraint satisfaction problems on finite domains are typically solved using a form of ___________ a) Search Algorithms b) Heuristic Search Algorithms c) Greedy Search Algorithms d) All of the mentioned

Description : Constraint satisfaction problems on finite domains are typically solved using a form of ___________ a) Search Algorithms b) Heuristic Search Algorithms c) Greedy Search Algorithms d) All of the mentioned

Last Answer : d) All of the mentioned

Greedy search strategy chooses the node for expansion in ___________ a) Shallowest b) Deepest c) The one closest to the goal node d) Minimum heuristic cost

Description : Greedy search strategy chooses the node for expansion in ___________ a) Shallowest b) Deepest c) The one closest to the goal node d) Minimum heuristic cost

Last Answer : c) The one closest to the goal node

__________ algorithm keeps track of k states rather than just one. a) Hill-Climbing search b) Local Beam search c) Stochastic hill-climbing search d) Random restart hill-climbing search

Description : __________ algorithm keeps track of k states rather than just one. a) Hill-Climbing search b) Local Beam search c) Stochastic hill-climbing search d) Random restart hill-climbing search

Last Answer : b) Local Beam search

What are the main cons of hill-climbing search? a) Terminates at local optimum & Does not find optimum solution b) Terminates at global optimum & Does not find optimum solution c) Does not find optimum solution & Fail to find a solution

Description : What are the main cons of hill-climbing search? a) Terminates at local optimum & Does not find optimum solution b) Terminates at global optimum & Does not find optimum solution c) Does not find optimum solution & Fail to find a solution

Last Answer : a) Terminates at local optimum & Does not find optimum solution

When will Hill-Climbing algorithm terminate? a) Stopping criterion met b) Global Min/Max is achieved c) No neighbor has higher value d) All of the mentioned

Description : When will Hill-Climbing algorithm terminate? a) Stopping criterion met b) Global Min/Max is achieved c) No neighbor has higher value d) All of the mentioned

Last Answer : c) No neighbor has higher value

_____________ algorithms is used to extract the plan directly from the planning graph, rather than using graph to provide heuristic. a) BFS/DFS b) A* c) Graph-Plan d) Greedy

Description : _____________ algorithms is used to extract the plan directly from the planning graph, rather than using graph to provide heuristic. a) BFS/DFS b) A* c) Graph-Plan d) Greedy

Last Answer : c) Graph-Plan

What is the evaluation function in greedy approach? 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

Description : What is the evaluation function in greedy approach? 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

Last Answer : a) Heuristic function

Which of the following search belongs to totally ordered plan search? a) Forward state-space search b) Hill-climbing search c) Depth-first search d) Breadth-first search

Description : Which of the following search belongs to totally ordered plan search? a) Forward state-space search b) Hill-climbing search c) Depth-first search d) Breadth-first search

Last Answer : a) Forward state-space search

Which is the most straightforward approach for planning algorithm? a) Best-first search b) State-space search c) Depth-first search d) Hill-climbing search

Description : Which is the most straightforward approach for planning algorithm? a) Best-first search b) State-space search c) Depth-first search d) Hill-climbing search

Last Answer : b) State-space search

What is the meaning for greedy local search?

Description : What is the meaning for greedy local search?

Last Answer :  It goals (picks) a good neighbor state without thinking ahead about where to go next. 

Which algorithm is used for solving temporal probabilistic reasoning? a) Hill-climbing search b) Hidden markov model c) Depth-first search d) Breadth-first search

Description : Which algorithm is used for solving temporal probabilistic reasoning? a) Hill-climbing search b) Hidden markov model c) Depth-first search d) Breadth-first search

Last Answer : b) Hidden markov model

Which is used to extract solution directly from the planning graph? a) Planning algorithm b) Graphplan c) Hill-climbing search d) All of the mentioned

Description : Which is used to extract solution directly from the planning graph? a) Planning algorithm b) Graphplan c) Hill-climbing search d) All of the mentioned

Last Answer : b) Graphplan

Which algorithm takes two sentences and returns a unifier? a) Inference b) Hill-climbing search c) Depth-first search d) Unify algorithm

Description : Which algorithm takes two sentences and returns a unifier? a) Inference b) Hill-climbing search c) Depth-first search d) Unify algorithm

Last Answer : d) Unify algorithm

Which algorithm are in more similar to backward chaining algorithm? a) Depth-first search algorithm b) Breadth-first search algorithm c) Hill-climbing search algorithm d) All of the mentioned

Description : Which algorithm are in more similar to backward chaining algorithm? a) Depth-first search algorithm b) Breadth-first search algorithm c) Hill-climbing search algorithm d) All of the mentioned

Last Answer : a) Depth-first search algorithm

Which search is similar to minimax search? a) Hill-climbing search b) Depth-first search c) Breadth-first search d) All of the mentioned

Description : Which search is similar to minimax search? a) Hill-climbing search b) Depth-first search c) Breadth-first search d) All of the mentioned

Last Answer : b) Depth-first search

Which of the following algorithm is generally used CSP search algorithm? a) Breadth-first search algorithm b) Depth-first search algorithm c) Hill-climbing search algorithm d) None of the mentioned

Description : Which of the following algorithm is generally used CSP search algorithm? a) Breadth-first search algorithm b) Depth-first search algorithm c) Hill-climbing search algorithm d) None of the mentioned

Last Answer : b) Depth-first search algorithm

______________ Is an algorithm, a loop that continually moves in the direction of increasing value – that is uphill. a) Up-Hill Search b) Hill-Climbing c) Hill algorithm d) Reverse-Down-Hill search

Description : ______________ Is an algorithm, a loop that continually moves in the direction of increasing value – that is uphill. a) Up-Hill Search b) Hill-Climbing c) Hill algorithm d) Reverse-Down-Hill search

Last Answer : b) Hill-Climbing

A* algorithm is based on ___________ a) Breadth-First-Search b) Depth-First –Search c) Best-First-Search d) Hill climbing

Description : A* algorithm is based on ___________ a) Breadth-First-Search b) Depth-First –Search c) Best-First-Search d) Hill climbing

Last Answer : c) Best-First-Search

Which search algorithm will use limited amount of memory? a) RBFS b) SMA* c) Hill-climbing search algorithm d) Both RBFS & SMA*

Description : Which search algorithm will use limited amount of memory? a) RBFS b) SMA* c) Hill-climbing search algorithm d) Both RBFS & SMA*

Last Answer : d) Both RBFS & SMA*

Define Hill Climbing search.

Description : Define Hill Climbing search.

Last Answer : It is a loop that continually moves in a increasing value direction (i.e.) up hill and terminates when it reaches a “peak” where no neighbor has a higher value. 

Which is the best way to go for Game playing problem? a) Linear approach b) Heuristic approach (Some knowledge is stored) c) Random approach d) An Optimal approach

Description : Which is the best way to go for Game playing problem? a) Linear approach b) Heuristic approach (Some knowledge is stored) c) Random approach d) An Optimal approach

Last Answer : b) Heuristic approach (Some knowledge is stored)

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

Description : 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

Last Answer : a) True

______ do not guarantee optimal/any solutions A. Heuristic B. Critical C. Value based D. Analytical

Description : ______ do not guarantee optimal/any solutions A. Heuristic B. Critical C. Value based D. Analytical 

Last Answer : A. Heuristic 

A complete, local search algorithm always finds goal if one exists, an optimal algorithm always finds a global minimum/maximum. a) True b) False

Description : A complete, local search algorithm always finds goal if one exists, an optimal algorithm always finds a global minimum/maximum. a) True b) False

Last Answer : a) True

What are the main cons of hill-climbing search? A : Terminates at local optimum & Does not find optimum solution B : Terminates at global optimum & Does not find optimum solution C : Does not find optimum solution & Fail to find a solution D : Fail to find a solution

Description : What are the main cons of hill-climbing search? A : Terminates at local optimum & Does not find optimum solution B : Terminates at global optimum & Does not find optimum solution C : Does not find optimum solution & Fail to find a solution D : Fail to find a solution

Last Answer : A : Terminates at local optimum & Does not find optimum solution

What are the main cons of hill-climbing search? A : Terminates at local optimum & Does not find optimum solution B : Terminates at global optimum & Does not find optimum solution C : Does not find optimum solution & Fail to find a solution D : Fail to find a solution

Description : What are the main cons of hill-climbing search? A : Terminates at local optimum & Does not find optimum solution B : Terminates at global optimum & Does not find optimum solution C : Does not find optimum solution & Fail to find a solution D : Fail to find a solution

Last Answer : A : Terminates at local optimum & Does not find optimum solution

What are the main cons of hill-climbing search? A : Terminates at local optimum & Does not find optimum solution B : Terminates at global optimum & Does not find optimum solution C : Does not find optimum solution & Fail to find a solution D : Fail to find a solution

Description : What are the main cons of hill-climbing search? A : Terminates at local optimum & Does not find optimum solution B : Terminates at global optimum & Does not find optimum solution C : Does not find optimum solution & Fail to find a solution D : Fail to find a solution

Last Answer : A : Terminates at local optimum & Does not find optimum solution

What is the best method to go for the game playing problem? (1) Optimal Search (2) Random Search (3) Heuristic Search (4) Stratified Search

Description : What is the best method to go for the game playing problem? (1) Optimal Search (2) Random Search (3) Heuristic Search (4) Stratified Search 

Last Answer : (3) Heuristic Search

Special programs that assist programmers are called ____________ a) heuristic processors b) symbolic programmers c) intelligent programming tools d) program recognizers

Description : Special programs that assist programmers are called ____________ a) heuristic processors b) symbolic programmers c) intelligent programming tools d) program recognizers

Last Answer : c) intelligent programming tools

Strategies that know whether one non-goal state is “more promising” than another are called ___________ a) Informed & Unformed Search b) Unformed Search c) Heuristic & Unformed Search d) Informed & Heuristic Search

Description : Strategies that know whether one non-goal state is “more promising” than another are called ___________ a) Informed & Unformed Search b) Unformed Search c) Heuristic & Unformed Search d) Informed & Heuristic Search

Last Answer : d) Informed & Heuristic Search

Breadth-first search is not optimal when all step costs are equal, because it always expands the shallowest unexpanded node. a) True b) False

Description : Breadth-first search is not optimal when all step costs are equal, because it always expands the shallowest unexpanded node. a) True b) False

Last Answer : b) False

Which search method will expand the node that is closest to the goal? a) Best-first search b) Greedy best-first search c) A* search d) None of the mentioned

Description : Which search method will expand the node that is closest to the goal? a) Best-first search b) Greedy best-first search c) A* search d) None of the mentioned

Last Answer : b) Greedy best-first search

Which function will select the lowest expansion node at first for evaluation? a) Greedy best-first search b) Best-first search c) Depth-first search d) None of the mentioned

Description : Which function will select the lowest expansion node at first for evaluation? a) Greedy best-first search b) Best-first search c) Depth-first search d) None of the mentioned

Last Answer : b) Best-first search

Define Greedy Best First Search.

Description : Define Greedy Best First Search.

Last Answer : It expands the node that is closest to the goal (i.e.) to reach solution in a quicker way. It is done by using the heuristic function: f(n) = h(n).

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

Description : 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

Last Answer : a) True

What are the variants of hill climbing?

Description : What are the variants of hill climbing?

Last Answer : i. Stochastic hill climbing  ii. First choice hill climbing  iii. Simulated annealing search  iv. Local beam search  v. Stochastic beam search

List some drawbacks of hill climbing process.

Description : List some drawbacks of hill climbing process. 

Last Answer : Local maxima: A local maxima as opposed to a goal maximum is a peak that is lower that the highest peak in the state space. Once a local maxima is reached the algorithm will halt even though ... of the state space where the evaluation fn is essentially flat. The search will conduct a random walk.

Which method is effective for escaping from local minima? a) Updating heuristic estimate b) Reducing heuristic estimate c) Eliminating heuristic estimate d) None of the mentioned

Description : Which method is effective for escaping from local minima? a) Updating heuristic estimate b) Reducing heuristic estimate c) Eliminating heuristic estimate d) None of the mentioned

Last Answer : a) Updating heuristic estimate

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) All of the mentioned

Description : 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) All of the mentioned

Last Answer : d) All 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

Description : 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

Last Answer : b) Quality of heuristic function

What is the other name of informed search strategy? a) Simple search b) Heuristic search c) Online search d) None of the mentioned

Description : What is the other name of informed search strategy? a) Simple search b) Heuristic search c) Online search d) None of the mentioned

Last Answer : b) Heuristic search

uniform-cost search expands the node n with the __________ a) Lowest path cost b) Heuristic cost c) Highest path cost d) Average path cost

Description : uniform-cost search expands the node n with the __________ a) Lowest path cost b) Heuristic cost c) Highest path cost d) Average path cost

Last Answer : a) Lowest path cost

What is the general term of Blind searching? a) Informed Search b) Uninformed Search c) Informed & Unformed Search d) Heuristic Search

Description : What is the general term of Blind searching? a) Informed Search b) Uninformed Search c) Informed & Unformed Search d) Heuristic Search

Last Answer : b) Uninformed Search

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 the other is d) All of the mentioned

Description : 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 the other is d) All of the mentioned

Last Answer : d) All of the mentioned

Genetic Algorithm are a part of A . Evolutionary Computing B. inspired by Darwin's theory about evolution - "survival of the fittest" C. are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics D . All of the above

Description : Genetic Algorithm are a part of A . Evolutionary Computing B. inspired by Darwin's theory about evolution - "survival of the fittest" C. are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics D . All of the above

Last Answer : D . All of the above

The ____ level contains constituents at the third level which are knowledge based system, heuristic search, automatic theorem proving, multi-agent system. A. Cognition level B. Gross level C. Functional level D. All of above

Description : The ____ level contains constituents at the third level which are knowledge based system, heuristic search, automatic theorem proving, multi-agent system. A. Cognition level B. Gross level C. Functional level D. All of above

Last Answer : B. Gross level 

A ____ is a rule of thumb, strategy, trick, simplification, or any other kind of device which drastically limits search for solutions in large problem spaces. A. Heuristic B. Critical C. Value based D. Analytical

Description : A ____ is a rule of thumb, strategy, trick, simplification, or any other kind of device which drastically limits search for solutions in large problem spaces. A. Heuristic B. Critical C. Value based D. Analytical

Last Answer : A. Heuristic 

Which search is complete and optimal when h(n) is consistent? a) Best-first search b) Depth-first search c) Both Best-first & Depth-first search d) A* search

Description : Which search is complete and optimal when h(n) is consistent? a) Best-first search b) Depth-first search c) Both Best-first & Depth-first search d) A* search

Last Answer : d) A* search

When is breadth-first search is optimal? a) When there is less number of nodes b) When all step costs are equal c) When all step costs are unequal

Description : When is breadth-first search is optimal? a) When there is less number of nodes b) When all step costs are equal c) When all step costs are unequal

Last Answer : b) When all step costs are equal