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

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
by

1 Answer

Answer :

d) All of the mentioned
by

Related questions

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

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 (D) Only (a), (b) and (c).

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 (D) Only (a), (b) and (c).

Last Answer : (D) Only (a), (b) and (c).

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 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 goal. All we can do is choose the node that appears to be best according to the evaluation function. a) True b) False

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

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

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

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

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 data structure is used to give better heuristic estimates? a) Forwards state-space b) Backward state-space c) Planning graph algorithm d) None of the mentioned

Description : Which data structure is used to give better heuristic estimates? a) Forwards state-space b) Backward state-space c) Planning graph algorithm d) None of the mentioned

Last Answer : c) Planning graph algorithm

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

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

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 

Choose from the following that are Decision Tree nodes? a) Decision Nodes b) End Nodes c) Chance Nodes d) All of the mentioned

Description : Choose from the following that are Decision Tree nodes? a) Decision Nodes b) End Nodes c) Chance Nodes d) All of the mentioned

Last Answer : d) All of the mentioned

what is heuristic function A : Lowest path cost B : Cheapest path from root to goal node C : Average path cost D : Estimated cost of cheapest path from root to goal node

Description : what is heuristic function A : Lowest path cost B : Cheapest path from root to goal node C : Average path cost D : Estimated cost of cheapest path from root to goal node

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

what is heuristic function A : Lowest path cost B : Cheapest path from root to goal node C : Average path cost D : Estimated cost of cheapest path from root to goal node

Description : what is heuristic function A : Lowest path cost B : Cheapest path from root to goal node C : Average path cost D : Estimated cost of cheapest path from root to goal node

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

what is heuristic function A : Lowest path cost B : Cheapest path from root to goal node C : Average path cost D : Estimated cost of cheapest path from root to goal node

Description : what is heuristic function A : Lowest path cost B : Cheapest path from root to goal node C : Average path cost D : Estimated cost of cheapest path from root to goal node

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

what is heuristic function A : Lowest path cost B : Cheapest path from root to goal node C : Average path cost D : Estimated cost of cheapest path from root to goal node

Description : what is heuristic function A : Lowest path cost B : Cheapest path from root to goal node C : Average path cost D : Estimated cost of cheapest path from root to goal node

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

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

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

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

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

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 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

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 

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) 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 d) None of the mentioned

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

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

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

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

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

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

How the effectiveness of the alpha-beta pruning gets increased? a) Depends on the nodes b) Depends on the order in which they are executed c) All of the mentioned d) None of the mentioned

Description : How the effectiveness of the alpha-beta pruning gets increased? a) Depends on the nodes b) Depends on the order in which they are executed c) All of the mentioned d) None of the mentioned

Last Answer : a) Depends on the nodes

Optimality of BFS is ___________ a) When there is less number of nodes b) When all step costs are equal c) When all step costs are unequal d) None of the mentioned

Description : Optimality of BFS is ___________ a) When there is less number of nodes b) When all step costs are equal c) When all step costs are unequal d) None of the mentioned

Last Answer : b) When all step costs are equal

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

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

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

Define search node.

Description : Define search node.

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

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 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

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

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

A network with named nodes and labeled arcs that can be used to represent certain natural language grammars to facilitate parsing. a) Tree Network b) Star Network c) Transition Network d) Complete Network

Description : A network with named nodes and labeled arcs that can be used to represent certain natural language grammars to facilitate parsing. a) Tree Network b) Star Network c) Transition Network d) Complete Network

Last Answer : c) Transition Network

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 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