Define search node.

1 Answer

Answer :

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

Related questions

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 : 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 : 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 : 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 : 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 : 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

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 : 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 : 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 : 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 : 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 : 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 :  The initial state and the legal moves for each side define the __________ for the game. a) Search Tree b) Game Tree c) State Space Search d) Forest

Last Answer : b) Game Tree

Description : Define Online Search agent. 

Last Answer : Agent operates by interleaving computation and action (i.e.) first it takes an action, and then it observes the environment and computes the next action.

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. 

Description : Define iterative deepening search.

Last Answer : Iterative deepening is a strategy that sidesteps the issue of choosing the best depth limit by trying all possible depth limits: first depth 0, then depth 1,then depth 2& so on.

Description : Define A* search.

Last Answer : A* search evaluates nodes by combining g(n), the cost to reach the node and h(n), the cost to get from the node to the goal. f(n) = g(n) + h(n)

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 : Define depth limited search.

Last Answer : The problem of unbounded tress can be avoided by supplying depth limit 1(i.e.) nodes at depth 1 are treated as if they have no successors. This is called Depth Limited search. 

Description : Define Depth first search.

Last Answer :  It expands the deepest node in the current fringe of the search tree.  

Description : Define Uniform cost search.

Last Answer : Uniform cost search expands the node ‘n’ with the lowest path cost instead of expanding the shallowest node. 

Description : Define Backtracking search.

Last Answer : The variant of depth first search called backtracking search. Only one successor is generated at a time rather than all successor, partially expanded node remembers which successor generate next is called Backtracking search.

Description : Define search tree.

Last Answer : The tree which is constructed for the search process over the state space is called search tree. 

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 : What is the consequence between a node and its predecessors while creating bayesian network? a) Functionally dependent b) Dependant c) Conditionally independent d) Both Conditionally dependant & Dependant

Last Answer : c) Conditionally independent

Description : One the main drawback of this type of planning system is that it requires a lot of computational powers at each node. a) True b) False

Last Answer : a) True

Description : Basic idea of an partitioned nets is to break network into spaces which consist of groups of nodes and arcs and regard each space as a node. a) True b) False

Last Answer : a) True

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 : 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 : 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 : 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 : What is the consequence between a node and its predecessors while creating Bayesian network? a) Conditionally dependent b) Dependent c) Conditionally independent d) Both a & b

Last Answer : c) Conditionally independent

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

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

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 : 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

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

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

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

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

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

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

Description : Inheritance Search a) True b) False

Last Answer : a) True

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 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

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

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

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