29 + Interview Questions in Adversarial Search in Artificial Intelligence Page 1 InterviewSolution

1.	Which function is used to calculate the feasibility of whole game tree?(a) Evaluation function(b) Transposition(c) Alpha-beta pruning(d) All of the mentionedThe question was posed to me in an interview for job.My doubt is from Alpha Beta Pruning topic in section Adversarial Search of Artificial Intelligence
Answer» Right OPTION is (a) Evaluation function To EXPLAIN: Because we NEED to cut the search off at some point and APPLY an evaluation function that gives an estimate of the utility of the state.

Discussion

2.	What is called as transposition table?(a) Hash table of next seen positions(b) Hash table of previously seen positions(c) Next value in the search(d) None of the mentionedI had been asked this question during an internship interview.This is a very interesting question from Alpha Beta Pruning in portion Adversarial Search of Artificial Intelligence
Answer» RIGHT CHOICE is (b) HASH table of previously seen positions For explanation I WOULD say: Transposition is the OCCURRENCE of repeated states frequently in the search.

Discussion

3.	Which is identical to the closed list in Graph search?(a) Hill climbing search algorithm(b) Depth-first search(c) Transposition table(d) None of the mentionedI had been asked this question in a job interview.This key question is from Alpha Beta Pruning topic in section Adversarial Search of Artificial Intelligence
Answer» CORRECT ANSWER is (C) Transposition table Easy explanation: None.

Discussion

4.	Where does the values of alpha-beta search get updated?(a) Along the path of search(b) Initial state itself(c) At the end(d) None of the mentionedThis question was posed to me in quiz.I want to ask this question from Alpha Beta Pruning topic in section Adversarial Search of Artificial Intelligence
Answer» Correct answer is (a) ALONG the path of search Explanation: ALPHA-beta search UPDATES the value of alpha and beta as it GETS along and prunes the remaining branches at node.

Discussion

5.	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 mentionedI had been asked this question by my school principal while I was bunking the class.This key question is from Alpha Beta Pruning in division Adversarial Search of Artificial Intelligence
Answer» RIGHT OPTION is (a) DEPENDS on the nodes Explanation: NONE.

Discussion

6.	Which value is assigned to alpha and beta in the alpha-beta pruning?(a) Alpha = max(b) Beta = min(c) Beta = max(d) Both Alpha = max & Beta = minThe question was posed to me in an interview for internship.Asked question is from Alpha Beta Pruning topic in portion Adversarial Search of Artificial Intelligence
Answer» Correct option is (d) Both Alpha = MAX & BETA = min Easy explanation: Alpha and beta are the values of the best CHOICE we have FOUND so far at any choice point along the path for MAX and MIN.

Discussion

7.	To which depth does the alpha-beta pruning can be applied?(a) 10 states(b) 8 States(c) 6 States(d) Any depthThis question was addressed to me in an interview for internship.The doubt is from Alpha Beta Pruning in chapter Adversarial Search of Artificial Intelligence
Answer» Right answer is (d) Any DEPTH Easiest explanation: Alpha–beta pruning can be applied to TREES of any depth and it is possible to PRUNE entire subtree RATHER than LEAVES.

Discussion

8.	Which search is similar to minimax search?(a) Hill-climbing search(b) Depth-first search(c) Breadth-first search(d) All of the mentionedThe question was posed to me in a job interview.Asked question is from Alpha Beta Pruning in chapter Adversarial Search of Artificial Intelligence
Answer» Correct option is (b) Depth-first search The explanation is: The minimax search is depth-first search, So at one time we just have to CONSIDER the NODES ALONG a single path in the TREE.

Discussion

9.	Which values are independant in minimax search algorithm?(a) Pruned leaves x and y(b) Every states are dependant(c) Root is independant(d) None of the mentionedThis question was addressed to me during an interview.My query is from Alpha Beta Pruning topic in portion Adversarial Search of Artificial Intelligence
Answer» The CORRECT OPTION is (a) Pruned leaves X and y Best EXPLANATION: The minimax DECISION are independant of the values of the pruned values x and y because of the root values.

Discussion

10.	Which search is equal to minimax search but eliminates the branches that can’t influence the final decision?(a) Depth-first search(b) Breadth-first search(c) Alpha-beta pruning(d) None of the mentionedI have been asked this question at a job interview.Origin of the question is Alpha Beta Pruning in division Adversarial Search of Artificial Intelligence
Answer» Correct choice is (C) Alpha-beta pruning The explanation: The alpha-beta SEARCH computes the same OPTIMAL moves as minimax, but eliminates the branches that can’t influence the final DECISION.

Discussion

11.	Which approach is to pretend that a pure divide and conquer algorithm will work?(a) Goal independence(b) Subgoal independence(c) Both Goal & Subgoal independence(d) None of the mentionedI have been asked this question in my homework.Query is from State Space Search in section Adversarial Search of Artificial Intelligence
Answer» Correct answer is (B) Subgoal independence To explain: Subgoal independence APPROACH is to PRETEND that a pure divide and conquer ALGORITHM will WORK for admissible heuristics.

Discussion

12.	What is the main advantage of backward state-space search?(a) Cost(b) Actions(c) Relevant actions(d) All of the mentionedThe question was asked in an interview.The query is from State Space Search topic in chapter Adversarial Search of Artificial Intelligence
Answer» The correct ANSWER is (c) Relevant actions Best explanation: The main ADVANTAGE of backward search will ALLOW us to consider only relevant actions.

Discussion

13.	What will happen if a predecessor description is generated that is satisfied by the initial state of the planning problem?(a) Success(b) Error(c) Compilation(d) TerminationI have been asked this question during an online exam.The question is from State Space Search topic in section Adversarial Search of Artificial Intelligence
Answer» CORRECT ANSWER is (d) Termination The BEST I can EXPLAIN: NONE.

Discussion

14.	What is meant by consistent in state-space search?(a) Change in the desired literals(b) Not any change in the literals(c) No change in goal state(d) None of the mentionedI had been asked this question in exam.This interesting question is from State Space Search topic in section Adversarial Search of Artificial Intelligence
Answer» The correct option is (b) Not any change in the LITERALS To explain: CONSISTENT MEANS that the COMPLETED actions will not undo any desired literals.

Discussion

15.	What is the other name of the backward state-space search?(a) Regression planning(b) Progression planning(c) State planning(d) Test planningThe question was asked in quiz.I need to ask this question from State Space Search topic in chapter Adversarial Search of Artificial Intelligence
Answer» Correct option is (a) Regression PLANNING For explanation: Backward state-space search will FIND the SOLUTION from goal to the action, So it is called as Regression planning.

Discussion

16.	How many states are available in state-space search?(a) 1(b) 2(c) 3(d) 4I have been asked this question during an interview.This question is from State Space Search topic in chapter Adversarial Search of Artificial Intelligence
Answer» RIGHT OPTION is (d) 4 The best I can explain: There are FOUR states available in STATE-space search. They are initial state, actions, goal test and STEP cost.

Discussion

17.	What is the other name for forward state-space search?(a) Progression planning(b) Regression planning(c) Test planning(d) None of the mentionedThis question was addressed to me in quiz.The doubt is from State Space Search in section Adversarial Search of Artificial Intelligence
Answer» The correct answer is (a) Progression PLANNING The EXPLANATION: It is sometimes CALLED as progression planning, because it moves in the forward direction.

Discussion

18.	Zero sum game has to be a ______ game.(a) Single player(b) Two player(c) Multiplayer(d) Three playerThis question was posed to me in an online interview.This question is from Game Theory in portion Adversarial Search of Artificial Intelligence
Answer» Correct ANSWER is (c) Multiplayer For explanation: ZERO sum games COULD be multiplayer games as LONG as the condition for zero sum GAME is satisfied.

Discussion

19.	How many ways are available to solve the state-space search?(a) 1(b) 2(c) 3(d) 4I had been asked this question in my homework.The query is from State Space Search in section Adversarial Search of Artificial Intelligence
Answer» The correct option is (b) 2 The BEST I can explain: There are two ways available to solve the state-space search. They are forward from the INITIAL state and BACKWARD from the goal.

Discussion

20.	What are taken into account of state-space search?(a) Postconditions(b) Preconditions(c) Effects(d) Both Preconditions & EffectsThis question was addressed to me in a national level competition.Question is taken from State Space Search topic in portion Adversarial Search of Artificial Intelligence
Answer» CORRECT answer is (d) Both Preconditions & EFFECTS Explanation: The state-space search takes both precondition and effects into account for solving a PROBLEM.

Discussion

21.	Which is the most straightforward approach for planning algorithm?(a) Best-first search(b) State-space search(c) Depth-first search(d) Hill-climbing searchThis question was addressed to me in semester exam.Enquiry is from State Space Search in section Adversarial Search of Artificial Intelligence
Answer» Right answer is (b) STATE-space search For explanation I WOULD SAY: The straightforward approach for planning ALGORITHM is state space search because it takes into account of everything for finding a solution.

Discussion

22.	The minimax algorithm computes the minimax decision from the current state. It uses a simple recursive computation of the minimax values of each successor state, directly implementing the defining equations. The recursion proceeds all the way down to the leaves of the tree, and then the minimax values are backed up through the tree as the recursion unwinds.(a) True(b) FalseI had been asked this question in homework.The doubt is from Game Theory in section Adversarial Search of Artificial Intelligence
Answer» Correct answer is (a) True The BEST EXPLANATION: Refer definition of MINIMAX algorithm.

Discussion

23.	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 AlgorithmsI had been asked this question in an online quiz.Enquiry is from Game Theory in section Adversarial Search of Artificial Intelligence
Answer» Correct answer is (d) MIN/MAX Algorithms To explain: GIVEN a game tree, the optimal strategy can be determined by examining the min/max value of each node, which we write as MINIMAX- VALUE(N). The min/max value of a node is the utility (for MAX) of being in the corresponding state, assuming that both PLAYERS play optimally from there to the end of the game. Obviously, the min/max value of a terminal state is just its utility. FURTHERMORE, given a choice, MAX will PREFER to move to a state of maximum value, whereas MIN prefers a state of minimum value.

Discussion

24.	Zero sum games are the one in which there are two agents whose actions must alternate and in which the utility values at the end of the game are always the same.(a) True(b) FalseThis question was posed to me in examination.I'm obligated to ask this question of Game Theory topic in chapter Adversarial Search of Artificial Intelligence
Answer» Correct ANSWER is (B) False Easiest explanation: UTILITY values are ALWAYS same and OPPOSITE.

Discussion

25.	The initial state and the legal moves for each side define the __________ for the game.(a) Search Tree(b) Game Tree(c) State Space Search(d) ForestI had been asked this question in examination.My query is from Game Theory in portion Adversarial Search of Artificial Intelligence
Answer» Correct OPTION is (b) Game TREE To ELABORATE: An EXAMPLE of game tree for Tic-Tac-Toe game.

Discussion

26.	A game can be formally defined as a kind of search problem with the following components.(a) Initial State(b) Successor Function(c) Terminal Test(d) All of the mentionedThe question was posed to me at a job interview.The doubt is from Game Theory in portion Adversarial Search of Artificial Intelligence
Answer» Right choice is (d) All of the mentioned The explanation: The initial state includes the BOARD position and identifies the player to move. A successor function returns a list of (move, state) pairs, each indicating a legal move and the RESULTING state. A terminal test determines when the game is over. States where the game has ended are CALLED terminal states. A utility function (also called an objective function or payoff function), which gives a numeric VALUE for the terminal states. In chess, the outcome is a win, lose, or draw, with VALUES +1, -1, or 0.

Discussion

27.	Mathematical game theory, a branch of economics, views any multi-agent environment as a game provided that the impact of each agent on the others is “significant,” regardless of whether the agents are cooperative or competitive.(a) True(b) FalseI got this question by my college professor while I was bunking the class.My query is from Game Theory in portion Adversarial Search of Artificial Intelligence
Answer» RIGHT CHOICE is (a) True Easiest EXPLANATION: NONE.

Discussion

28.	Adversarial search problems uses ____________(a) Competitive Environment(b) Cooperative Environment(c) Neither Competitive nor Cooperative Environment(d) Only Competitive and Cooperative EnvironmentThe question was posed to me in an international level competition.This intriguing question comes from Game Theory in chapter Adversarial Search of Artificial Intelligence
Answer» CORRECT answer is (a) Competitive Environment To explain: SINCE in cooperative environment agents’ goals are I CONFLICTS. They COMPETE for goal.

Discussion

29.	General games involves ____________(a) Single-agent(b) Multi-agent(c) Neither Single-agent nor Multi-agent(d) Only Single-agent and Multi-agentI got this question during an interview for a job.My doubt is from Game Theory in portion Adversarial Search of Artificial Intelligence
Answer» Right answer is (d) Only SINGLE-agent and Multi-agent Explanation: DEPENDING upon GAMES it could be single agent (SUDOKU) or multi-agent (Chess).

Discussion

Explore topic-wise InterviewSolutions in .

Where does the values of alpha-beta search get updated?(a) Along the path of search(b) Initial state itself(c) At the end(d) None of the mentionedThis question was posed to me in quiz.I want to ask this question from Alpha Beta Pruning topic in section Adversarial Search of Artificial Intelligence

To which depth does the alpha-beta pruning can be applied?(a) 10 states(b) 8 States(c) 6 States(d) Any depthThis question was addressed to me in an interview for internship.The doubt is from Alpha Beta Pruning in chapter Adversarial Search of Artificial Intelligence

Which search is similar to minimax search?(a) Hill-climbing search(b) Depth-first search(c) Breadth-first search(d) All of the mentionedThe question was posed to me in a job interview.Asked question is from Alpha Beta Pruning in chapter Adversarial Search of Artificial Intelligence

What is the main advantage of backward state-space search?(a) Cost(b) Actions(c) Relevant actions(d) All of the mentionedThe question was asked in an interview.The query is from State Space Search topic in chapter Adversarial Search of Artificial Intelligence

What is the other name of the backward state-space search?(a) Regression planning(b) Progression planning(c) State planning(d) Test planningThe question was asked in quiz.I need to ask this question from State Space Search topic in chapter Adversarial Search of Artificial Intelligence

How many states are available in state-space search?(a) 1(b) 2(c) 3(d) 4I have been asked this question during an interview.This question is from State Space Search topic in chapter Adversarial Search of Artificial Intelligence

What is the other name for forward state-space search?(a) Progression planning(b) Regression planning(c) Test planning(d) None of the mentionedThis question was addressed to me in quiz.The doubt is from State Space Search in section Adversarial Search of Artificial Intelligence

Zero sum game has to be a ______ game.(a) Single player(b) Two player(c) Multiplayer(d) Three playerThis question was posed to me in an online interview.This question is from Game Theory in portion Adversarial Search of Artificial Intelligence

How many ways are available to solve the state-space search?(a) 1(b) 2(c) 3(d) 4I had been asked this question in my homework.The query is from State Space Search in section Adversarial Search of Artificial Intelligence

What are taken into account of state-space search?(a) Postconditions(b) Preconditions(c) Effects(d) Both Preconditions & EffectsThis question was addressed to me in a national level competition.Question is taken from State Space Search topic in portion Adversarial Search of Artificial Intelligence

Which is the most straightforward approach for planning algorithm?(a) Best-first search(b) State-space search(c) Depth-first search(d) Hill-climbing searchThis question was addressed to me in semester exam.Enquiry is from State Space Search in section Adversarial Search of Artificial Intelligence

The initial state and the legal moves for each side define the __________ for the game.(a) Search Tree(b) Game Tree(c) State Space Search(d) ForestI had been asked this question in examination.My query is from Game Theory in portion Adversarial Search of Artificial Intelligence

General games involves ____________(a) Single-agent(b) Multi-agent(c) Neither Single-agent nor Multi-agent(d) Only Single-agent and Multi-agentI got this question during an interview for a job.My doubt is from Game Theory in portion Adversarial Search of Artificial Intelligence