The term ___________ is used for a depth-first search that chooses values for one variable at a time and returns when a variable has no legal values left to assign. a) Forward search b) Backtrack search c) Hill algorithm d) Reverse-Down-Hill search

The term ___________ is used for a depth-first search that chooses values for one variable at a time

and returns when a variable has no legal values left to assign.

a) Forward search

b) Backtrack search

c) Hill algorithm

d) Reverse-Down-Hill search
by

1 Answer

Answer :

b) Backtrack search
by

Related questions

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

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

______________ 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

To overcome the need to backtrack in constraint satisfaction problem can be eliminated by ____________ a) Forward Searching b) Constraint Propagation c) Backtrack after a forward search d) Omitting the constraints and focusing only on goals

Description : To overcome the need to backtrack in constraint satisfaction problem can be eliminated by ____________ a) Forward Searching b) Constraint Propagation c) Backtrack after a forward search d) Omitting the constraints and focusing only on goals

Last Answer : a) Forward Searching

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 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 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

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

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

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

An algorithm A is admissible if ___________ a) It is not guaranteed to return an optimal solution when one exists b) It is guaranteed to return an optimal solution when one exists c) It returns more solutions, but not an optimal one d) It guarantees to return more optimal solutions

Description : An algorithm A is admissible if ___________ a) It is not guaranteed to return an optimal solution when one exists b) It is guaranteed to return an optimal solution when one exists c) It returns more solutions, but not an optimal one d) It guarantees to return more optimal solutions

Last Answer : b) It is guaranteed to return an optimal solution when one exists

Which algorithm will work backward from the goal to solve a problem? a) Forward chaining b) Backward chaining c) Hill-climb algorithm d) None of the mentioned

Description : Which algorithm will work backward from the goal to solve a problem? a) Forward chaining b) Backward chaining c) Hill-climb algorithm d) None of the mentioned

Last Answer : b) Backward chaining

A search algorithm takes _________ as an input and returns ________ as an output. a) Input, output b) Problem, solution c) Solution, problem d) Parameters, sequence of actions

Description : A search algorithm takes _________ as an input and returns ________ as an output. a) Input, output b) Problem, solution c) Solution, problem d) Parameters, sequence of actions

Last Answer : b) Problem, solution

Computers normally solve problem by breaking them down into a series of yes-or-no decisions represented by 1s and 0s. What is the name of the logic that allows computers to assign numerical values that fail somewhere between 0 and 1? a) Human logic b) Fuzzy logic c) Boolean logic d) Operational logic

Description : Computers normally solve problem by breaking them down into a series of yes-or-no decisions represented by 1s and 0s. What is the name of the logic that allows computers to assign numerical values that fail ... 0 and 1? a) Human logic b) Fuzzy logic c) Boolean logic d) Operational logic

Last Answer : b) Fuzzy logic

Consider a problem of preparing a schedule for a class of student. What type of problem is this? a) Search Problem b) Backtrack Problem c) CSP d) Planning Problem

Description : Consider a problem of preparing a schedule for a class of student. What type of problem is this? a) Search Problem b) Backtrack Problem c) CSP d) Planning Problem

Last Answer : c) CSP

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

__________ 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

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*

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

Which search algorithm imposes a fixed depth limit on nodes? a) Depth-limited search b) Depth-first search c) Iterative deepening search d) Bidirectional search

Description : Which search algorithm imposes a fixed depth limit on nodes? a) Depth-limited search b) Depth-first search c) Iterative deepening search d) Bidirectional search

Last Answer : a) Depth-limited search

The BACKTRACKING-SEARCH algorithm in Figure 5.3 has a very simple policy for what to do when a branch of the search fails: back up to the preceding variable and try a different value for it. This is called chronological-backtracking. It is also possible to go all the way to set of variable that caused failure. a) True b) False

Description : The BACKTRACKING-SEARCH algorithm in Figure 5.3 has a very simple policy for what to do when a branch of the search fails: back up to the preceding variable and try a different value for it. This is ... also possible to go all the way to set of variable that caused failure. a) True b) False

Last Answer : a) True

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 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

A* algorithm is based on which of the following concept? A : Best-First-Search B : Breadth-First-Search C : Depth-First –Search D : Hill climbing

Description : A* algorithm is based on which of the following concept? A : Best-First-Search B : Breadth-First-Search C : Depth-First –Search D : Hill climbing

Last Answer : A : Best-First-Search

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

The minimax algorithm computes the minimax decision from the current state. It uses a simple recursive computation of the minimax values of each successor state, directly implementing the defining equations. The recursion proceeds all the way down to the leaves of the tree, and then the minimax values are backed up through the tree as the recursion unwinds. a) True b) False

Description : The minimax algorithm computes the minimax decision from the current state. It uses a simple recursive computation of the minimax values of each successor state, directly implementing the defining equations. The ... are backed up through the tree as the recursion unwinds. a) True b) False

Last Answer : a) True

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

In LISP, the function returns t if <integer> is even and nil otherwise ___________ a) (evenp <integer>) b) (even <integer>) c) (numeven <integer>) d) (numnevenp <integer>)

Description : In LISP, the function returns t if is even and nil otherwise ___________ a) (evenp ) b) (even ) c) (numeven ) d) (numnevenp )

Last Answer : a) (evenp )

The Set of actions for a problem in a state space is formulated by a ___________ a) Intermediate states b) Initial state c) Successor function, which takes current action and returns next immediate state d) None of the mentioned

Description : The Set of actions for a problem in a state space is formulated by a ___________ a) Intermediate states b) Initial state c) Successor function, which takes current action and returns next immediate state d) None of the mentioned

Last Answer : c) Successor function, which takes current action and returns next immediate state

A perceptron is a ______________ a) Feed-forward neural network b) Backpropagation algorithm c) Backtracking algorithm d) Feed Forward-backward algorithm

Description : A perceptron is a ______________ a) Feed-forward neural network b) Backpropagation algorithm c) Backtracking algorithm d) Feed Forward-backward algorithm

Last Answer : a) Feed-forward neural network

A perceptron is a ——————————–. a) Feed-forward neural network b) Back-propagation algorithm c) Back-tracking algorithm d) Feed Forward-backward algorithm e) Optimal algorithm with Dynamic programming

Description : A perceptron is a ——————————–. a) Feed-forward neural network b) Back-propagation algorithm c) Back-tracking algorithm d) Feed Forward-backward algorithm e) Optimal algorithm with Dynamic programming

Last Answer : a) Feed-forward neural network

Which is omitted in prolog unification algorithm? a) Variable check b) Occur check c) Proposition check d) Both Occur & Proposition check

Description : Which is omitted in prolog unification algorithm? a) Variable check b) Occur check c) Proposition check d) Both Occur & Proposition check

Last Answer : b) Occur check

Quantitative attributes are A. A reference to the speed of an algorithm, which is quadratically dependent on the size of the data B. Attributes of a database table that can take only numerical values C. Tools designed to query a database D. None of these

Description : Quantitative attributes are A. A reference to the speed of an algorithm, which is quadratically dependent on the size of the data B. Attributes of a database table that can take only numerical values C. Tools designed to query a database D. None of these

Last Answer : B. Attributes of a database table that can take only numerical values

Which algorithm is used to solve any kind of problem? a) Breadth-first algorithm b) Tree algorithm c) Bidirectional search algorithm d) None of the mentioned

Description : Which algorithm is used to solve any kind of problem? a) Breadth-first algorithm b) Tree algorithm c) Bidirectional search algorithm d) None of the mentioned

Last Answer : b) Tree algorithm

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

A genetic algorithm (or GA) is a variant of stochastic beam search in which successor states are generated by combining two parent states, rather than by modifying a single state. a) True b) False

Description : A genetic algorithm (or GA) is a variant of stochastic beam search in which successor states are generated by combining two parent states, rather than by modifying a single state. a) True b) False

Last Answer : a) True

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

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

Forward chaining systems are _____________ where as backward chaining systems are ___________ a) Goal-driven, goal-driven b) Goal-driven, data-driven c) Data-driven, goal-driven d) Data-driven, data-driven

Description : Forward chaining systems are _____________ where as backward chaining systems are ___________ a) Goal-driven, goal-driven b) Goal-driven, data-driven c) Data-driven, goal-driven d) Data-driven, data-driven

Last Answer : c) Data-driven, goal-driven

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

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

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. 

A Hybrid Bayesian network contains ___________ a) Both discrete and continuous variables b) Only Discrete variables c) Only Discontinuous variable

Description : A Hybrid Bayesian network contains ___________ a) Both discrete and continuous variables b) Only Discrete variables c) Only Discontinuous variable

Last Answer : a) Both discrete and continuous variables

Decision trees are appropriate for the problems where ___________ a) Attributes are both numeric and nominal b) Target function takes on a discrete number of values. c) Data may have errors d) All of the mentioned

Description : Decision trees are appropriate for the problems where ___________ a) Attributes are both numeric and nominal b) Target function takes on a discrete number of values. c) Data may have errors d) All of the mentioned

Last Answer : d) All of the mentioned

The values of the set membership is represented by ___________ a) Discrete Set b) Degree of truth c) Probabilities d) Both Degree of truth & Probabilities

Description : The values of the set membership is represented by ___________ a) Discrete Set b) Degree of truth c) Probabilities d) Both Degree of truth & Probabilities

Last Answer : b) Degree of truth

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

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

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

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

The time and space complexity of BFS is (For time and space complexity problems consider b as branching factor and d as depth of the search tree.) a) O(bd+1) and O(bd+1) b) O(b2) and O(d2) c) O(d2) and O(b2) d) O(d2) and O(d2)

Description : The time and space complexity of BFS is (For time and space complexity problems consider b as branching factor and d as depth of the search tree.) a) O(bd+1) and O(bd+1) b) O(b2) and O(d2) c) O(d2) and O(b2) d) O(d2) and O(d2)

Last Answer : a) O(bd+1) and O(bd+1)