88 + Interview Questions in Problem Solving in Artificial Intelligence Page 1 InterviewSolution

1.	When do we call the states are safely explored?(a) A goal state is unreachable from any state(b) A goal state is denied access(c) A goal state is reachable from every state(d) None of the mentionedI got this question during a job interview.This interesting question is from Constraints Satisfaction Problems topic in portion Problem Solving of Artificial Intelligence
Answer» RIGHT ANSWER is (c) A goal state is REACHABLE from every state To explain: NONE.

Discussion

2.	Constraint Propagation technique actually modifies the CSP problem.(a) True(b) FalseThe question was posed to me by my school teacher while I was bunking the class.My question is taken from Constraints Satisfaction Problems in division Problem Solving of Artificial Intelligence
Answer» The correct option is (a) True The BEST explanation: CONSTRAINTS are PROPAGATED TOWARDS goal node, modifying the actual PROBLEM.

Discussion

3.	Backtracking is based on ____________(a) Last in first out(b) First in first out(c) Recursion(d) Both Last in first out & RecursionI got this question in quiz.My question comes from Constraints Satisfaction Problems topic in chapter Problem Solving of Artificial Intelligence
Answer» The CORRECT option is (d) Both Last in first out & Recursion To EXPLAIN I would SAY: Recursion USES LIFO.

Discussion

4.	Language/Languages used for programming Constraint Programming includes ____________(a) Prolog(b) C#(c) C(d) FortrunI have been asked this question in an interview for job.The query is from Constraints Satisfaction Problems topic in section Problem Solving of Artificial Intelligence
Answer» Correct ANSWER is (a) Prolog For EXPLANATION I would say: NONE.

Discussion

5.	Flexible CSPs relax on _______(a) Constraints(b) Current State(c) Initial State(d) Goal StateI had been asked this question by my college professor while I was bunking the class.The origin of the question is Constraints Satisfaction Problems topic in division Problem Solving of Artificial Intelligence
Answer» The CORRECT choice is (a) Constraints Best explanation: DEFINITION of FLEXIBLE CSPS.

Discussion

6.	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 ProblemI have been asked this question in an interview.The doubt is from Constraints Satisfaction Problems in portion Problem Solving of Artificial Intelligence
Answer» Correct choice is (c) CSP To EXPLAIN: Schedule DEVELOPER NEEDS to CONSIDER all constraints on teacher as well as STUDENTS.

Discussion

7.	____________ 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 mentionedI had been asked this question in a job interview.I'd like to ask this question from Constraints Satisfaction Problems topic in division Problem Solving of Artificial Intelligence
Answer» RIGHT answer is (b) Dynamic CSPs For explanation: REFER to the DEFINITION of Dynamic CSPs algorithm.

Discussion

8.	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(d) Domain dependentThe question was asked in final exam.Query is from Constraints Satisfaction Problems in chapter Problem Solving of Artificial Intelligence
Answer» The CORRECT ANSWER is (B) NP complete Explanation: NONE.

Discussion

9.	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 mentionedThis question was posed to me during an interview.My doubt is from Constraints Satisfaction Problems topic in chapter Problem Solving of Artificial Intelligence
Answer» CORRECT answer is (d) All of the mentioned Easiest explanation: Any SEARCH TECHNIQUES can be USED

Discussion

10.	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) FalseI had been asked this question in a national level competition.Question is from Constraints Satisfaction Problems topic in section Problem Solving of Artificial Intelligence
Answer» RIGHT ANSWER is (a) True To ELABORATE: INTELLIGENT BACKTRACKING

Discussion

11.	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 goalsThis question was addressed to me by my school teacher while I was bunking the class.The doubt is from Constraints Satisfaction Problems topic in division Problem Solving of Artificial Intelligence
Answer» Right answer is (a) Forward Searching Best explanation: Forward Searching is technique in which a forward check till k STEPS is made to analyze that the goal can be ACHIEVED satiating all constraints. With constraint propagation, constraints on a variable can be PROPAGATED to NEXT level/hierarchy and satisfied at that level, eliminating need to backtrack.

Discussion

12.	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 searchThis question was posed to me in an interview for internship.Asked question is from Constraints Satisfaction Problems topic in division Problem Solving of Artificial Intelligence
Answer» Right ANSWER is (B) BACKTRACK search Easiest explanation: Refer definition of backtracking algorithm.

Discussion

13.	Which of the Following problems can be modeled as CSP?(a) 8-Puzzle problem(b) 8-Queen problem(c) Map coloring problem(d) All of the mentionedThis question was addressed to me during an online interview.Query is from Constraints Satisfaction Problems topic in portion Problem Solving of Artificial Intelligence
Answer» Right answer is (d) All of the mentioned The BEST explanation: All of above problems involves CONSTRAINTS to be SATISFIED.

Discussion

14.	What among the following constitutes to the incremental formulation of CSP?(a) Path cost(b) Goal cost(c) Successor function(d) All of the mentionedI have been asked this question in an online quiz.Question is from Constraints Satisfaction Problems topic in section Problem Solving of Artificial Intelligence
Answer» Right answer is (d) All of the mentioned The best I can explain: Initial state: The EMPTY assignment ( ), in which all variables are unassigned. Successor function: A value can be assigned to any unassigned variable, provided it does not CONFLICT with PREVIOUSLY assigned variables. Goal test: The current assignment is complete. Path cost: A CONSTANT cost (e.g., 1) for every STEP.

Discussion

15.	_________________ 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 mentionedI have been asked this question in semester exam.Question is from Constraints Satisfaction Problems topic in chapter Problem Solving of Artificial Intelligence
Answer» The correct ANSWER is (a) Constraints SATISFACTION Problems The explanation: REFER definition of CSPs.

Discussion

16.	What are the two main features of Genetic Algorithm?(a) Fitness function & Crossover techniques(b) Crossover techniques & Random mutation(c) Individuals among the population & Random mutation(d) Random mutation & Fitness functionI have been asked this question at a job interview.Question is from Local Search Problems and Optimization Problems topic in chapter Problem Solving of Artificial Intelligence
Answer» The correct CHOICE is (a) Fitness FUNCTION & Crossover techniques Best EXPLANATION: Fitness function helps choosing individuals from the population and Crossover techniques DEFINES the offspring GENERATED.

Discussion

17.	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) FalseThe question was asked in my homework.This key question is from Local Search Problems and Optimization Problems in division Problem Solving of Artificial Intelligence
Answer» Correct choice is (a) True For explanation I would say: STOCHASTIC beam search, analogous to stochastic hill climbing, helps to alleviate this PROBLEM. INSTEAD of choosing the best k from the pool of candidate successors, stochastic beam search chooses k successors at random, with the PROBABILITY of choosing a given successor being an increasing function of its value.

Discussion

18.	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 searchThe question was asked in an interview.The query is from Local Search Problems and Optimization Problems in section Problem Solving of Artificial Intelligence
Answer» The correct OPTION is (C) Greedy LOCAL search To explain: NONE.

Discussion

19.	___________ 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 searchI have been asked this question in a national level competition.My question is based upon Local Search Problems and Optimization Problems in chapter Problem Solving of Artificial Intelligence
Answer» The correct ANSWER is (b) Local Beam SEARCH To EXPLAIN: Refer to the DEFINITION of Local Beam Search ALGORITHM.

Discussion

20.	Hill-Climbing approach stuck for which of the following reasons?(a) Local maxima(b) Ridges(c) Plateaux(d) All of the mentionedI had been asked this question in a job interview.My question is taken from Local Search Problems and Optimization Problems in portion Problem Solving of Artificial Intelligence
Answer» Correct option is (d) All of the mentioned For explanation: Local maxima: a local maximum is a PEAK that is higher than each of its neighboring states, but lower than the global maximum.Ridges: Ridges result in a SEQUENCE of local maxima that is very difficult for greedy ALGORITHMS to NAVIGATE. Plateaux: a plateau is an area of the state space landscape where the evaluation function is flat.

Discussion

21.	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) FalseI had been asked this question in homework.I need to ask this question from Local Search Problems and Optimization Problems topic in chapter Problem Solving of Artificial Intelligence
Answer» The CORRECT ANSWER is (a) True For EXPLANATION: REFER to the definition of variants of hill-climbing SEARCH.

Discussion

22.	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(d) Fail to find a solutionThis question was addressed to me in unit test.This question is from Local Search Problems and Optimization Problems topic in portion Problem Solving of Artificial Intelligence
Answer» Correct ANSWER is (a) Terminates at local OPTIMUM & Does not FIND optimum solution Explanation: ALGORITHM terminates at local optimum values, hence fails to find optimum solution.

Discussion

23.	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 mentionedThe question was asked by my college professor while I was bunking the class.I need to ask this question from Local Search Problems and Optimization Problems in division Problem Solving of Artificial Intelligence
Answer» The CORRECT option is (c) No neighbor has HIGHER VALUE The best I can explain: When no neighbor is having higher value, algorithm terminates FETCHING local min/max.

Discussion

24.	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 spaceI had been asked this question during an online exam.I want to ask this question from Local Search Problems and Optimization Problems topic in portion Problem Solving of Artificial Intelligence
Answer» Right answer is (d) Less memory & Finds a solution in LARGE infinite space Easy EXPLANATION: TWO advantages: (1) they use very little memory-usually a constant amount; and (2) they can often find reasonable solutions in large or infinite (CONTINUOUS) state spaces for which SYSTEMATIC algorithms are unsuitable.

Discussion

25.	_______________ 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 searchI had been asked this question in an interview for internship.Query is from Local Search Problems and Optimization Problems in section Problem Solving of Artificial Intelligence
Answer» Right ANSWER is (B) Hill-Climbing Easiest explanation: REFER the DEFINITION of Hill-Climbing approach.

Discussion

26.	In many problems the path to goal is irrelevant, this class of problems can be solved using ____________(a) Informed Search Techniques(b) Uninformed Search Techniques(c) Local Search Techniques(d) Informed & Uninformed Search TechniquesI have been asked this question by my college director while I was bunking the class.This interesting question is from Local Search Problems and Optimization Problems topic in section Problem Solving of Artificial Intelligence
Answer» The correct option is (C) Local Search Techniques For explanation: If the path to the GOAL does not matter, we might consider a different class of algorithms, ones that do not worry about paths at all. Local search algorithms OPERATE using a SINGLE current STATE (rather than multiple paths) and generally move only to neighbors of that state.

Discussion

27.	A complete, local search algorithm always finds goal if one exists, an optimal algorithm always finds a global minimum/maximum.(a) True(b) FalseThis question was addressed to me in an online quiz.My question is based upon Local Search Problems and Optimization Problems in section Problem Solving of Artificial Intelligence
Answer» Right answer is (a) True To elaborate: An ALGORITHM is COMPLETE if it FINDS a solution if EXISTS and optimal if finds optimal GOAL (minimum or maximum).

Discussion

28.	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 costI have been asked this question in an online quiz.This key question is from Informed Search and Exploration topic in chapter Problem Solving of Artificial Intelligence
Answer» The correct ANSWER is (c) Path cost from start NODE to current node + Heuristic cost The explanation: The most widely-known form of best-first search is called A* search. It 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). Since g(n) gives the path cost from the start node to node n, and h(n) is the estimated cost of the cheapest path from n to the goal.

Discussion

29.	What is the space complexity of Greedy search?(a) O(b)(b) O(bl)(c) O(m)(d) O(bm)I got this question during an online interview.I'm obligated to ask this question of Informed Search and Exploration topic in chapter Problem Solving of Artificial Intelligence
Answer» Correct answer is (d) O(bm) Best explanation: O(bm) is the space complexity where b is the BRANCHING factor and m is the MAXIMUM depth of the SEARCH tree. SINCE this algorithm resembles the DFS.

Discussion

30.	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 costThis question was addressed to me in an online interview.The question is from Informed Search and Exploration in division Problem Solving of Artificial Intelligence
Answer» The correct option is (a) Heuristic function To elaborate: Greedy best-first search3 tries to expand the node that is closest to the goal, on the grounds that this is LIKELY to lead to a solution quickly. THUS, it evaluates NODES by USING just the heuristic function: f (n) = h(n).

Discussion

31.	Greedy search strategy chooses the node for expansion in ___________(a) Shallowest(b) Deepest(c) The one closest to the goal node(d) Minimum heuristic costI have been asked this question in quiz.This key question is from Informed Search and Exploration topic in chapter Problem Solving of Artificial Intelligence
Answer» The correct choice is (c) The ONE closest to the goal node To explain: Sometimes minimum heuristics can be used, sometimes maximum heuristics function can be used. It DEPENDS UPON the application on which the ALGORITHM is APPLIED.

Discussion

32.	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 costThis question was posed to me during an interview.My question is based upon Informed Search and Exploration topic in section Problem Solving of Artificial Intelligence
Answer» Correct answer is (C) Estimated COST of CHEAPEST path from root to GOAL node The best EXPLANATION: Heuristic is an estimated cost.

Discussion

33.	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) FalseThe question was posed to me in a job interview.The above asked question is from Informed Search and Exploration topic in section Problem Solving of Artificial Intelligence
Answer» Correct CHOICE is (a) True To explain: If the EVALUATION function is exactly accurate, then this will indeed be the best NODE; in reality, the evaluation function will sometimes be off, and can LEAD the search ASTRAY.

Discussion

34.	Best-First search can be implemented using the following data structure.(a) Queue(b) Stack(c) Priority Queue(d) Circular QueueI had been asked this question by my school teacher while I was bunking the class.This intriguing question comes from Informed Search and Exploration in section Problem Solving of Artificial Intelligence
Answer» The correct option is (c) Priority Queue The best EXPLANATION: Best-first SEARCH can be implemented within our GENERAL search framework VIA a priority queue, a data structure that will maintain the FRINGE in ascending order of f-values.

Discussion

35.	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 applicableI had been asked this question in semester exam.This interesting question is from Informed Search and Exploration topic in chapter Problem Solving of Artificial Intelligence
Answer» The CORRECT OPTION is (a) Evaluation FUNCTION RETURNING lowest evaluation Best explanation: Best-first search is an instance of the general TREE-SEARCH or GRAPH-SEARCH algorithm in which a node is selected for expansion based on an evaluation function, f (N). Traditionally, the node with the lowest evaluation is selected for expansion, because the evaluation measures distance to the goal.

Discussion

36.	Uninformed search strategies are better than informed search strategies.(a) True(b) FalseThis question was posed to me by my school teacher while I was bunking the class.This intriguing question comes from Informed Search and Exploration in portion Problem Solving of Artificial Intelligence
Answer» The correct choice is (B) False To explain I WOULD say: Informed SEARCH strategies USES some problem specific knowledge, hence more efficient to finding goals.

Discussion

37.	The search strategy the uses a problem specific knowledge is known as ___________(a) Informed Search(b) Best First Search(c) Heuristic Search(d) All of the mentionedI have been asked this question by my school teacher while I was bunking the class.This interesting question is from Informed Search and Exploration in division Problem Solving of Artificial Intelligence
Answer» The CORRECT answer is (d) All of the mentioned To EXPLAIN I WOULD say: The problem specific KNOWLEDGE is ALSO known as Heuristics and Best-First search uses some heuristic to choose the best node for expansion.

Discussion

38.	A* algorithm is based on ___________(a) Breadth-First-Search(b) Depth-First –Search(c) Best-First-Search(d) Hill climbingI had been asked this question during an online interview.Origin of the question is Informed Search and Exploration in section Problem Solving of Artificial Intelligence
Answer» The correct answer is (c) Best-First-Search To explain I would SAY: Best-first-search is giving the idea of optimization and quick CHOOSE of path, and all these characteristic LIES in A* algorithm.

Discussion

39.	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 mentionedThis question was posed to me during an internship interview.I'm obligated to ask this question of Informed Search Strategy topic in division Problem Solving of Artificial Intelligence
Answer» The CORRECT OPTION is (B) Greedy best-first search The best explanation: Because of using greedy best-first search, It will QUICKLY lead to the SOLUTION of the problem.

Discussion

40.	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 mentionedThe question was posed to me in an international level competition.I want to ask this question from Informed Search Strategy topic in division Problem Solving of Artificial Intelligence
Answer» Correct ANSWER is (b) QUALITY of heuristic function Best EXPLANATION: GOOD heuristic can be constructed by relaxing the problem, So the performance of heuristic search can be IMPROVED.

Discussion

41.	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 mentionedI have been asked this question in a job interview.My question is taken from Informed Search and Exploration in section Problem Solving of Artificial Intelligence
Answer» The CORRECT answer is (d) All of the mentioned Explanation: In a heuristic approach, we discover certain IDEA and use heuristic functions to search for a GOAL and PREDICATES to compare nodes.

Discussion

42.	Which search is complete and optimal when h(n) is consistent?(a) Best-first search(b) Depth-first search(c) Both Best-first & Depth-first search(d) A* searchThis question was posed to me in an interview for internship.Question is taken from Informed Search Strategy topic in portion Problem Solving of Artificial Intelligence
Answer» The CORRECT CHOICE is (d) A* search Explanation: NONE.

Discussion

43.	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 mentionedI got this question in final exam.This interesting question is from Informed Search Strategy in portion Problem Solving of Artificial Intelligence
Answer» Right OPTION is (c) Metalevel state space The explanation is: This search STRATEGY will HELP to problem solving efficiency by using LEARNING.

Discussion

44.	Which search uses only the linear space for searching?(a) Best-first search(b) Recursive best-first search(c) Depth-first search(d) None of the mentionedI had been asked this question by my school principal while I was bunking the class.I'd like to ask this question from Informed Search Strategy topic in section Problem Solving of Artificial Intelligence
Answer» The correct CHOICE is (b) Recursive best-first search Easy explanation: Recursive best-first search will MIMIC the OPERATION of STANDARD best-first search, but using only the linear space.

Discussion

45.	What is the heuristic function of greedy best-first search?(a) f(n) != h(n)(b) f(n) < h(n)(c) f(n) = h(n)(d) f(n) > h(n)I have been asked this question in a national level competition.My question comes from Informed Search Strategy topic in division Problem Solving of Artificial Intelligence
Answer» RIGHT option is (C) f(N) = h(n) The BEST I can explain: None.

Discussion

46.	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 mentionedThis question was posed to me in quiz.The question is from Informed Search Strategy in chapter Problem Solving of Artificial Intelligence
Answer» Right answer is (B) Best-first search Explanation: The lowest expansion node is SELECTED because the evaluation MEASURES DISTANCE to the goal.

Discussion

47.	Which search uses the problem specific knowledge beyond the definition of the problem?(a) Informed search(b) Depth-first search(c) Breadth-first search(d) Uninformed searchThis question was addressed to me during an internship interview.My question is based upon Informed Search Strategy in portion Problem Solving of Artificial Intelligence
Answer» RIGHT option is (a) Informed SEARCH The best explanation: Informed search can SOLVE the PROBLEM beyond the function definition, So does it can find the SOLUTION more efficiently.

Discussion

48.	Breadth-first search always expands the ______ node in the current fringe of the search tree.(a) Shallowest(b) Child node(c) Deepest(d) Minimum costThis question was posed to me during an interview.I need to ask this question from Uninformed Search and Exploration topic in portion Problem Solving of Artificial Intelligence
Answer» Correct ANSWER is (a) Shallowest Easiest explanation: Breadth-first search always EXPANDS the shallowest node in the current FRINGE of the search tree. Traversal is performed LEVEL WISE.

Discussion

49.	How many types of informed search method are in artificial intelligence?(a) 1(b) 2(c) 3(d) 4I got this question during an interview for a job.Question is from Informed Search Strategy topic in chapter Problem Solving of Artificial Intelligence
Answer» Correct option is (d) 4 Explanation: The four TYPES of informed search method are best-first search, Greedy best-first search, A* search and MEMORY BOUNDED HEURISTIC search.

Discussion

50.	What is the other name of informed search strategy?(a) Simple search(b) Heuristic search(c) Online search(d) None of the mentionedThis question was posed to me by my college director while I was bunking the class.This key question is from Informed Search Strategy topic in portion Problem Solving of Artificial Intelligence
Answer» Correct choice is (B) Heuristic SEARCH The BEST I can explain: A key point of informed search strategy is heuristic function, So it is called as heuristic function.

Discussion

Explore topic-wise InterviewSolutions in .

Constraint Propagation technique actually modifies the CSP problem.(a) True(b) FalseThe question was posed to me by my school teacher while I was bunking the class.My question is taken from Constraints Satisfaction Problems in division Problem Solving of Artificial Intelligence

Backtracking is based on ____________(a) Last in first out(b) First in first out(c) Recursion(d) Both Last in first out & RecursionI got this question in quiz.My question comes from Constraints Satisfaction Problems topic in chapter Problem Solving of Artificial Intelligence

Language/Languages used for programming Constraint Programming includes ____________(a) Prolog(b) C#(c) C(d) FortrunI have been asked this question in an interview for job.The query is from Constraints Satisfaction Problems topic in section Problem Solving of Artificial Intelligence

What is the space complexity of Greedy search?(a) O(b)(b) O(bl)(c) O(m)(d) O(bm)I got this question during an online interview.I'm obligated to ask this question of Informed Search and Exploration topic in chapter Problem Solving of Artificial Intelligence

Uninformed search strategies are better than informed search strategies.(a) True(b) FalseThis question was posed to me by my school teacher while I was bunking the class.This intriguing question comes from Informed Search and Exploration in portion Problem Solving of Artificial Intelligence

A* algorithm is based on ___________(a) Breadth-First-Search(b) Depth-First –Search(c) Best-First-Search(d) Hill climbingI had been asked this question during an online interview.Origin of the question is Informed Search and Exploration in section Problem Solving of Artificial Intelligence

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 mentionedI got this question in final exam.This interesting question is from Informed Search Strategy in portion Problem Solving of Artificial Intelligence

What is the heuristic function of greedy best-first search?(a) f(n) != h(n)(b) f(n) < h(n)(c) f(n) = h(n)(d) f(n) > h(n)I have been asked this question in a national level competition.My question comes from Informed Search Strategy topic in division Problem Solving of Artificial Intelligence

How many types of informed search method are in artificial intelligence?(a) 1(b) 2(c) 3(d) 4I got this question during an interview for a job.Question is from Informed Search Strategy topic in chapter Problem Solving of Artificial Intelligence