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

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
by

1 Answer

Answer :

d) All of the mentioned
by

Related questions

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

Solving a constraint satisfaction problem on a finite domain is an/a ___________ problem with respect to the domain size. a) P complete b) NP complete c) NP hard

Description : Solving a constraint satisfaction problem on a finite domain is an/a ___________ problem with respect to the domain size. a) P complete b) NP complete c) NP hard

Last Answer : b) NP complete

_____________ 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

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

The map colouring problem can be solved using which of the following technique? (A) Means-end analysis (B) Constraint satisfaction (C) AO* search (D) Breadth first search

Description : The map colouring problem can be solved using which of the following technique? (A) Means-end analysis (B) Constraint satisfaction (C) AO* search (D) Breadth first search

Last Answer : (B) Constraint satisfaction

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

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

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

_______________ are mathematical problems defined as a set of objects whose state must satisfy a number of constraints or limitations. a) Constraints Satisfaction Problems b) Uninformed Search Problems c) Local Search Problems d) All of the mentioned

Description : _______________ are mathematical problems defined as a set of objects whose state must satisfy a number of constraints or limitations. a) Constraints Satisfaction Problems b) Uninformed Search Problems c) Local Search Problems d) All of the mentioned

Last Answer : a) Constraints Satisfaction Problems

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 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

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 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*)

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

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

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

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

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

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

Standard planning algorithms assumes environment to be ___________ a) Deterministic b) Fully observable c) Single agent d) Stochastic

Description : Standard planning algorithms assumes environment to be ___________ a) Deterministic b) Fully observable c) Single agent d) Stochastic

Last Answer : a) Deterministic

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).

__________ 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

Though local search algorithms are not systematic, key advantages would include __________ a) Less memory b) More time c) Finds a solution in large infinite space d) Less memory & Finds a solution in large infinite space

Description : Though local search algorithms are not systematic, key advantages would include __________ a) Less memory b) More time c) Finds a solution in large infinite space d) Less memory & Finds a solution in large infinite space

Last Answer : d) Less memory & Finds a solution in large infinite space

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 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 

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 

____________ are algorithms that learn from their more complex environments (hence eco) to generalize, approximate and simplify solution logic. a) Fuzzy Relational DB b) Ecorithms c) Fuzzy Set d) None of the mentioned

Description : ____________ are algorithms that learn from their more complex environments (hence eco) to generalize, approximate and simplify solution logic. a) Fuzzy Relational DB b) Ecorithms c) Fuzzy Set d) None of the mentioned

Last Answer : c) Fuzzy Set

______ are algorithms that learn from their more complex environments (hence eco) to generalize, approximate and simplify solution logic. a) Fuzzy Relational DB b) Ecorithms c) Fuzzy Set d) None of the mentioned

Description : ______ are algorithms that learn from their more complex environments (hence eco) to generalize, approximate and simplify solution logic. a) Fuzzy Relational DB b) Ecorithms c) Fuzzy Set d) None of the mentioned

Last Answer : c) Fuzzy Set

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 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

Which will solve the conjuncts of the rule so that the total cost is minimized? a) Constraint variable b) Conjunct ordering c) Data complexity d) All of the mentioned

Description : Which will solve the conjuncts of the rule so that the total cost is minimized? a) Constraint variable b) Conjunct ordering c) Data complexity d) All of the mentioned

Last Answer : b) Conjunct ordering

While a poor physical distribution system would affect all elements of a company's marketing mix, the problems associated with a poor system A)can usually be solved by investing more money in the system. B)are usually caused by increased distribution costs. C)can have a negative impact on customer service and satisfaction. D)are usually attributed to problems with order processing.

Description : While a poor physical distribution system would affect all elements of a company's marketing mix, the problems associated with a poor system A)can usually be solved by investing more money ... impact on customer service and satisfaction. D)are usually attributed to problems with order processing.

Last Answer : C)can have a negative impact on customer service and satisfaction.

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

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

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

Why is the XOR problem exceptionally interesting to neural network researchers? a) Because it can be expressed in a way that allows you to use a neural network b) Because it is complex binary operation that cannot be solved using neural networks c) Because it can be solved by a single layer perceptron d) Because it is the simplest linearly inseparable problem that exists.

Description : Why is the XOR problem exceptionally interesting to neural network researchers? a) Because it can be expressed in a way that allows you to use a neural network b) Because it is complex ... solved by a single layer perceptron d) Because it is the simplest linearly inseparable problem that exists.

Last Answer : d) Because it is the simplest linearly inseparable problem that exists.

This set of Artificial Intelligence MCQs focuses on “Neural Networks – 2”. 1. Why is the XOR problem exceptionally interesting to neural network researchers? a) Because it can be expressed in a way that allows you to use a neural network b) Because it is complex binary operation that cannot be solved using neural networks c) Because it can be solved by a single layer perceptron d) Because it is the simplest linearly inseparable problem that exists.

Description : This set of Artificial Intelligence MCQs focuses on Neural Networks - 2 . 1. Why is the XOR problem exceptionally interesting to neural network researchers? a) Because it can be expressed in ... by a single layer perceptron d) Because it is the simplest linearly inseparable problem that exists.

Last Answer : d) Because it is the simplest linearly inseparable problem that exists.

____________ is/are useful when the original formulation of a problem is altered in some way, typically because the set of constraints to consider evolves because of the environment. a) Static CSPs b) Dynamic CSPs c) Flexible CSPs d) None of the mentioned

Description : ____________ is/are useful when the original formulation of a problem is altered in some way, typically because the set of constraints to consider evolves because of the environment. a) Static CSPs b) Dynamic CSPs c) Flexible CSPs d) None of the mentioned

Last Answer : b) Dynamic CSPs

A KES knowledge base contains information in the form of ___________ a) associations b) actions c) free text d) all of the mentioned

Description : A KES knowledge base contains information in the form of ___________ a) associations b) actions c) free text d) all of the mentioned

Last Answer : d) all of the mentioned

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

Description : 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

Last Answer : b) Backtrack 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

In constraint satisfaction problem, constraints can be stated as . (A) Arithmatic equations and inequalities that bind the values of variables (B) Arithmatic equations and inequalities that doesn’t bind any restriction over variables (C) Arithmatic equations that impose restrictions over variables (D) Arithmatic equations that discard constraints over the given variables

Description : In constraint satisfaction problem, constraints can be stated as . (A) Arithmatic equations and inequalities that bind the values of variables (B) Arithmatic equations and inequalities ... impose restrictions over variables (D) Arithmatic equations that discard constraints over the given variables

Last Answer : (A) Arithmatic equations and inequalities that bind the values of variables

Modern NLP algorithms are based on machine learning, especially statistical machine learning. a) True b) False

Description : Modern NLP algorithms are based on machine learning, especially statistical machine learning. a) True b) False

Last Answer : a) True

Which can be adapted for planning algorithms? a) Most-constrained variable b) Most-constrained literal

Description : Which can be adapted for planning algorithms? a) Most-constrained variable b) Most-constrained literal

Last Answer : a) Most-constrained variable

Distinguish between A* and AO* algorithms?

Description : Distinguish between A* and AO* algorithms?

Last Answer : Ans:- A* algorithm:-  A* uses a best-first search and finds the least-cost path from a given initial node to one goal node (out of one or more possible goals).  It uses a distance-plus- ... node as SOLVED.  If starting node is SOLVED or value greater than FUTILITY, stop, else repeat from 2.

Which of the following(s) is/are found in Genetic Algorithms? (i) evolution (ii) selection (iii) reproduction (iv) mutation (a) i & ii only (b) i, ii & iii only (c) ii, iii & iv only

Description : Which of the following(s) is/are found in Genetic Algorithms? (i) evolution (ii) selection (iii) reproduction (iv) mutation (a) i & ii only (b) i, ii & iii only (c) ii, iii & iv only

Last Answer : (a) i & ii only