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

1 Answer

Answer :

b) Best-first search

Related questions

Description : 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 ... c) Evaluation function returning lowest & highest evaluation d) None of them is applicable

Last Answer : a) Evaluation function returning lowest evaluation

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

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

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

Description : Best-First search is a type of informed search, which of the following principle used to choose the best next node for expansion A : Evaluation function returning lowest evaluation B : ... highest evaluation C : Evaluation function returning lowest & highest evaluation D : no evaluation function

Last Answer : A : Evaluation function returning lowest evaluation

Description : Best-First search is a type of informed search, which of the following principle used to choose the best next node for expansion A : Evaluation function returning lowest evaluation B : ... highest evaluation C : Evaluation function returning lowest & highest evaluation D : no evaluation function

Last Answer : A : Evaluation function returning lowest evaluation

Description : Best-First search is a type of informed search, which of the following principle used to choose the best next node for expansion A : Evaluation function returning lowest evaluation B : ... highest evaluation C : Evaluation function returning lowest & highest evaluation D : no evaluation function

Last Answer : A : Evaluation function returning lowest evaluation

Description : 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 ... is choose the node that appears to be best according to the evaluation function. a) True b) False

Last Answer : a) True

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

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

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

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

Description : Depth-first search always expands the ______ node in the current fringe of the search tree. a) Shallowest b) Child node c) Deepest d) Minimum cost

Last Answer : c) Deepest

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

Description : What is the evaluation function in A* 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 : c) Path cost from start node to current node + Heuristic cost

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

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

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).

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

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

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. 

Description : 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

Last Answer : c) Estimated cost of cheapest path from root to goal node

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

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

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

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

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

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

Description : 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

Last Answer : c) Metalevel state space

Description : 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

Last Answer : b) Recursive best-first search

Description : Which of the following is/are Uninformed Search technique/techniques? a) Breadth First Search (BFS) b) Depth First Search (DFS) c) Bidirectional Search d) All of the mentioned

Last Answer : d) All of the mentioned

Description : Which search implements stack operation for searching the states? a) Depth-limited search b) Depth-first search c) Breadth-first search d) None of the mentioned

Last Answer : b) Depth-first search

Description : Which search is implemented with an empty first-in-first-out queue? a) Depth-first search b) Breadth-first search c) Bidirectional search d) None of the mentioned

Last Answer : b) Breadth-first search

Description : Which function is used to calculate the feasibility of whole game tree? a) Evaluation function b) Transposition c) Alpha-beta pruning d) All of the mentioned

Last Answer : a) Evaluation function

Description : Breadth-first search always expands the ______ node in the current fringe of the search tree. a) Shallowest b) Child node c) Deepest

Last Answer : a) Shallowest

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

Description : Mention the criteria for the evaluation of search strategy.

Last Answer :  There are 4 criteria: Completeness, time complexity, space complexity, optimality

Description : The main idea of Bidirectional search is to reduce the time complexity by searching two way simultaneously from start node and another from goal node. a) True b) False

Last Answer : a) True

Description : Define search node.

Last Answer : The root of the search tree that is the initial state of the problem is called search node. 

Description : What is Decision Tree? a) Flow-Chart b) Structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label c) ... branch represents outcome of test and each leaf node represents class label d) None of the mentioned

Last Answer : c) Flow-Chart & Structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label

Description : Which is true for neural networks? a) It has set of nodes and connections b) Each node computes it’s weighted input c) Node could be in excited state or non-excited state d) All of the mentioned

Last Answer : d) All of the mentioned

Description : Which is true for neural networks? a) It has set of nodes and connections b) Each node computes it’s weighted input c) Node could be in excited state or non-excited state d) All of the mentioned

Last Answer : d) All of the mentioned

Description : A game can be formally defined as a kind of search problem with the following components. a) Initial State b) Successor Function c) Terminal Test d) All of the mentioned

Last Answer : d) All 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

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

Description : When will further expansion is unnecessary for planning graph? a) Identical b) Replicate c) Not identical d) None of the mentioned

Last Answer : a) Identical

Description : Artificial Intelligence has its expansion in the following application. a) Planning and Scheduling b) Game Playing c) Diagnosis d) All of the mentioned

Last Answer : d) All of the mentioned

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

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

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