What is the meaning for greedy local search?

What is the meaning for greedy local search?
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1 Answer

Answer :

 It goals (picks) a good neighbor state without thinking ahead about where to go next. 
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Related questions

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

Description : 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

Last Answer : c) Greedy local search

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

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).

__________ algorithm translates a planning problem in to prepositional axioms. a) GraphPlan b) SatPlan c) Greedy d) None of the mentioned

Description : __________ algorithm translates a planning problem in to prepositional axioms. a) GraphPlan b) SatPlan c) Greedy d) None of the mentioned

Last Answer : b) SatPlan

_____________ 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 among the following could the universal instantiation of ___________ For all x King(x) ^ Greedy(x) => Evil(x) a) King(John) ^ Greedy(John) => Evil(John) b) King(y) ^ Greedy(y) => Evil(y) c) King(Richard) ^ Greedy(Richard) => Evil(Richard) d) All of the mentioned

Description : What among the following could the universal instantiation of ___________ For all x King(x) ^ Greedy(x) => Evil(x) a) King(John) ^ Greedy(John) => Evil(John) b) King(y) ^ Greedy(y) => Evil(y) c) King(Richard) ^ Greedy(Richard) => Evil(Richard) d) All of the mentioned

Last Answer : d) All of the mentioned

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

_______________ are mathematical problems defined as a set of objects whose state must satisfy a number of constraints or limitations. a) Constraints Satisfaction Problems b) Uninformed Search Problems c) Local Search Problems d) All of the mentioned

Description : _______________ are mathematical problems defined as a set of objects whose state must satisfy a number of constraints or limitations. a) Constraints Satisfaction Problems b) Uninformed Search Problems c) Local Search Problems d) All of the mentioned

Last Answer : a) Constraints Satisfaction Problems

__________ 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

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

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) Less memory & Finds a solution in large infinite space

Description : 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) Less memory & Finds a solution in large infinite space

Last Answer : d) Less memory & Finds a solution in large infinite space

What is local search?

Description : What is local search?

Last Answer : It operates using a single current state rather than multiple paths and generally moves only to neighbors of that state.

Consider f(N) = g(N) + h(N) Where function g is a measure of the cost of getting from the start node to the current node N and h is an estimate of additional cost of getting from the current node N to the goal node. Then f(N) = h(N) is used in which one of the following algorithms ? (A) A* algorithm (B) AO* algorithm (C) Greedy best first search algorithm (D) Iterative A* algorithm

Description : Consider f(N) = g(N) + h(N) Where function g is a measure of the cost of getting from the start node to the current node N and h is an estimate of additional cost of getting from the current ... ? (A) A* algorithm (B) AO* algorithm (C) Greedy best first search algorithm (D) Iterative A* algorithm

Last Answer : (C) Greedy best first search algorithm

Which condition is used to influence a variable directly by all the others? a) Partially connected b) Fully connected c) Local connected d) None of the mentioned

Description : Which condition is used to influence a variable directly by all the others? a) Partially connected b) Fully connected c) Local connected d) None of the mentioned

Last Answer : b) Fully connected

To which does the local structure is associated? a) Hybrid b) Dependant c) Linear d) None of the mentioned

Description : To which does the local structure is associated? a) Hybrid b) Dependant c) Linear d) None of the mentioned

Last Answer : c) Linear

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

Description : 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

Last Answer : c) Partial & local Information

What kind of behavior does the stochastic environment posses? a) Local b) Deterministic c) Rational d) Primary

Description : What kind of behavior does the stochastic environment posses? a) Local b) Deterministic c) Rational d) Primary

Last Answer : a) Local

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

Which condition is used to influence a variable directly by all the others? a) Partially connected b) Fully connected c) Local connected d) None of the mentioned

Description : Which condition is used to influence a variable directly by all the others? a) Partially connected b) Fully connected c) Local connected d) None of the mentioned

Last Answer : b) Fully connected

Define Local maxima.

Description : Define Local maxima.

Last Answer : A local maximum is a peak that is higher than each of its neighboring states, but lower than the global maximum.

In which of the following situations might a blind search be acceptable? a) Real life situation b) Complex game c) Small search space d) All of the mentioned

Description : In which of the following situations might a blind search be acceptable? a) Real life situation b) Complex game c) Small search space d) All of the mentioned

Last Answer : c) Small search space

Which is true regarding BFS (Breadth First Search)? a) BFS will get trapped exploring a single path b) The entire tree so far been generated must be stored in BFS c) BFS is not guaranteed to find a solution if exists d) BFS is nothing but Binary First Search

Description : Which is true regarding BFS (Breadth First Search)? a) BFS will get trapped exploring a single path b) The entire tree so far been generated must be stored in BFS c) BFS is not guaranteed to find a solution if exists d) BFS is nothing but Binary First Search

Last Answer : b) The entire tree so far been generated must be stored in BFS

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

What are the two major aspects which combines AI Planning problem? a) Search & Logic b) Logic & Knowledge Based Systems c) FOL & Logic d) Knowledge Based Systems

Description : What are the two major aspects which combines AI Planning problem? a) Search & Logic b) Logic & Knowledge Based Systems c) FOL & Logic d) Knowledge Based Systems

Last Answer : a) Search & Logic

To eliminate the inaccuracy problem in planning problem or partial order planning problem we can use ___________________ data structure/s. a) Stacks b) Queue c) BST (Binary Search Tree) d) Planning Graphs

Description : To eliminate the inaccuracy problem in planning problem or partial order planning problem we can use ___________________ data structure/s. a) Stacks b) Queue c) BST (Binary Search Tree) d) Planning Graphs

Last Answer : d) Planning Graphs

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 strategy is used for delaying a choice during search? a) First commitment b) Least commitment c) Both First & Least commitment d) None of the mentioned

Description : Which strategy is used for delaying a choice during search? a) First commitment b) Least commitment c) Both First & Least commitment d) None of the mentioned

Last Answer : b) Least commitment

Which cannot be taken as advantage for totally ordered plan search? a) Composition b) State search c) Problem decomposition d) None of the mentioned

Description : Which cannot be taken as advantage for totally ordered plan search? a) Composition b) State search c) Problem decomposition d) None of the mentioned

Last Answer : c) Problem decomposition

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 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

There exists two way to infer using semantic networks in which knowledge is represented as Frames. 1) Intersection Search 2) Inheritance Search a) True b) False

Description : There exists two way to infer using semantic networks in which knowledge is represented as Frames. 1) Intersection Search 2) Inheritance Search a) True b) False

Last Answer : 1) Intersection Search

Inheritance Search a) True b) False

Description : Inheritance Search a) True b) False

Last Answer : a) True

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 sentence will be unsatisfiable if the CNF sentence is unsatisfiable? a) Search statement b) Reading statement c) Replaced statement d) Original statement

Description : Which sentence will be unsatisfiable if the CNF sentence is unsatisfiable? a) Search statement b) Reading statement c) Replaced statement d) Original statement

Last Answer : d) Original statement

A _________ is used to demonstrate, on a purely syntactic basis, that one formula is a logical consequence of another formula. a) Deductive Systems b) Inductive Systems c) Reasoning with Knowledge Based Systems d) Search Based Systems

Description : A _________ is used to demonstrate, on a purely syntactic basis, that one formula is a logical consequence of another formula. a) Deductive Systems b) Inductive Systems c) Reasoning with Knowledge Based Systems d) Search Based Systems

Last Answer : a) Deductive Systems

What is called as transposition table? a) Hash table of next seen positions b) Hash table of previously seen positions c) Next value in the search d) None of the mentioned

Description : What is called as transposition table? a) Hash table of next seen positions b) Hash table of previously seen positions c) Next value in the search d) None of the mentioned

Last Answer : b) Hash table of previously seen positions

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 values are independant in minimax search algorithm? a) Pruned leaves x and y b) Every states are dependant c) Root is independant d) None of the mentioned

Description : Which values are independant in minimax search algorithm? a) Pruned leaves x and y b) Every states are dependant c) Root is independant d) None of the mentioned

Last Answer : a) Pruned leaves x and y

Which search is equal to minimax search but eliminates the branches that can’t influence the final decision? a) Depth-first search b) Breadth-first search c) Alpha-beta pruning d) None of the mentioned

Description : Which search is equal to minimax search but eliminates the branches that can’t influence the final decision? a) Depth-first search b) Breadth-first search c) Alpha-beta pruning d) None of the mentioned

Last Answer : c) Alpha-beta pruning

What is the other name of the backward state-space search? a) Regression planning b) Progression planning c) State planning d) Test planning

Description : What is the other name of the backward state-space search? a) Regression planning b) Progression planning c) State planning d) Test planning

Last Answer : a) Regression planning

What is the main advantage of backward state-space search? a) Cost b) Actions c) Relevant actions d) All of the mentioned

Description : What is the main advantage of backward state-space search? a) Cost b) Actions c) Relevant actions d) All of the mentioned

Last Answer : c) Relevant actions

How many states are available in state-space search? a) 1 b) 2 c) 3 d) 4

Description : How many states are available in state-space search? a) 1 b) 2 c) 3 d) 4

Last Answer : d) 4

What is the other name for forward state-space search? a) Progression planning b) Regression planning c) Test planning d) None of the mentioned

Description : What is the other name for forward state-space search? a) Progression planning b) Regression planning c) Test planning d) None of the mentioned

Last Answer : a) Progression planning

How many ways are available to solve the state-space search? a) 1 b) 2 c) 3 d) 4

Description : How many ways are available to solve the state-space search? a) 1 b) 2 c) 3 d) 4

Last Answer : b) 2

What are taken into account of state-space search? a) Postconditions b) Preconditions c) Effects d) Both Preconditions & Effects

Description : What are taken into account of state-space search? a) Postconditions b) Preconditions c) Effects d) Both Preconditions & Effects

Last Answer : d) Both Preconditions & Effects