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

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
by

1 Answer

Answer :

a) Heuristic function
by

Related questions

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

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

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

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

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

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

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

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

_____________ 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

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

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

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

Merge sort uses a) Divide-and-conquer b) Backtracking c) Heuristic approach d) Greedy approach

Description : Merge sort uses a) Divide-and-conquer b) Backtracking c) Heuristic approach d) Greedy approach

Last Answer : a) Divide-and-conquer

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

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 is 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

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

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

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)

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

Define Heuristic function, h (n).

Description : Define Heuristic function, h (n).

Last Answer :  h (n) is defined as the estimated cost of the cheapest path from node n to a goal node. 

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 problem solving approach works well for ______________ a) 8-Puzzle problem b) 8-queen problem c) Finding a optimal path from a given source to a destination d) Mars Hover (Robot Navigation)

Description : A problem solving approach works well for ______________ a) 8-Puzzle problem b) 8-queen problem c) Finding a optimal path from a given source to a destination d) Mars Hover (Robot Navigation)

Last Answer : d) Mars Hover (Robot Navigation)

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

__________ 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

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 meaning for 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. 

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).

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

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 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

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

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 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

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

What is the term used for describing the judgmental or commonsense part of problem solving? a) Heuristic b) Critical c) Value based d) Analytical

Description : What is the term used for describing the judgmental or commonsense part of problem solving? a) Heuristic b) Critical c) Value based d) Analytical

Last Answer : a) Heuristic

An Artificial Intelligence technique that allows computers to understand associations and relationships between objects and events is called _____________ a) heuristic processing b) cognitive science c) relative symbolism d) pattern matching

Description : An Artificial Intelligence technique that allows computers to understand associations and relationships between objects and events is called _____________ a) heuristic processing b) cognitive science c) relative symbolism d) pattern matching

Last Answer : c) relative symbolism

What are the two subfields of Natural language processing? a) symbolic and numeric b) time and motion c) algorithmic and heuristic d) understanding and generation

Description : What are the two subfields of Natural language processing? a) symbolic and numeric b) time and motion c) algorithmic and heuristic d) understanding and generation

Last Answer : c) algorithmic and heuristic

Which term is used for describing the judgmental or commonsense part of problem solving? a) Heuristic b) Critical c) Value based d) Analytical

Description : Which term is used for describing the judgmental or commonsense part of problem solving? a) Heuristic b) Critical c) Value based d) Analytical

Last Answer : a) Heuristic

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

An AI technique that allows computers to understand associations and relationships between objects and events is called: a. heuristic processing b. cognitive science c. relative symbolism d. pattern matching

Description : An AI technique that allows computers to understand associations and relationships between objects and events is called: a. heuristic processing b. cognitive science c. relative symbolism d. pattern matching

Last Answer : d. pattern matching

A bidirectional feedback loop links computer modelling with: a. artificial science b. heuristic processing c. human intelligence d. cognitive science

Description : A bidirectional feedback loop links computer modelling with: a. artificial science b. heuristic processing c. human intelligence d. cognitive science

Last Answer : d. cognitive science

What are the 2 types of memory bounded heuristic algorithms?

Description : What are the 2 types of memory bounded heuristic algorithms?

Last Answer : i. Recursive Best First Search(RBFS)  ii. Memory bounded A*(MA*)

Define Admissible heuristic h (n).

Description : Define Admissible heuristic h (n).

Last Answer : In A* search, if it is optimal then, h(n) is an admissible heuristic which means h(n) never overestimates the cost to reach the goal.

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 

______ do not guarantee optimal/any solutions A. Heuristic B. Critical C. Value based D. Analytical

Description : ______ do not guarantee optimal/any solutions A. Heuristic B. Critical C. Value based D. Analytical 

Last Answer : A. Heuristic