Description : Which algorithm is used for solving temporal probabilistic reasoning? a) Hill-climbing search b) Hidden markov model c) Depth-first search d) Breadth-first search
Last Answer : b) Hidden markov model
Description : Which is used to extract solution directly from the planning graph? a) Planning algorithm b) Graphplan c) Hill-climbing search d) All of the mentioned
Last Answer : b) Graphplan
Description : Which of the following search belongs to totally ordered plan search? a) Forward state-space search b) Hill-climbing search c) Depth-first search d) Breadth-first search
Last Answer : a) Forward state-space search
Description : Which algorithm takes two sentences and returns a unifier? a) Inference b) Hill-climbing search c) Depth-first search d) Unify algorithm
Last Answer : d) Unify algorithm
Description : Which algorithm are in more similar to backward chaining algorithm? a) Depth-first search algorithm b) Breadth-first search algorithm c) Hill-climbing search algorithm d) All of the mentioned
Last Answer : a) Depth-first search algorithm
Description : Which search is similar to minimax search? a) Hill-climbing search b) Depth-first search c) Breadth-first search d) All of the mentioned
Last Answer : b) Depth-first search
Description : Which is the most straightforward approach for planning algorithm? a) Best-first search b) State-space search c) Depth-first search d) Hill-climbing search
Last Answer : b) State-space search
Description : Which of the following algorithm is generally used CSP search algorithm? a) Breadth-first search algorithm b) Depth-first search algorithm c) Hill-climbing search algorithm d) None of the mentioned
Last Answer : b) Depth-first search algorithm
Description : __________ 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 search
Last Answer : b) Local Beam 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
Description : 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
Last Answer : a) Terminates at local optimum & Does not find optimum solution
Description : ______________ 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 search
Last Answer : b) 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
Description : Which search algorithm will use limited amount of memory? a) RBFS b) SMA* c) Hill-climbing search algorithm d) Both RBFS & SMA*
Last Answer : d) Both RBFS & SMA*
Description : 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) False
Last Answer : a) True
Description : 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 mentioned
Last Answer : c) No neighbor has higher value
Description : What are the variants of hill climbing?
Last Answer : i. Stochastic hill climbing ii. First choice hill climbing iii. Simulated annealing search iv. Local beam search v. Stochastic beam search
Description : List some drawbacks of hill climbing process.
Last Answer : Local maxima: A local maxima as opposed to a goal maximum is a peak that is lower that the highest peak in the state space. Once a local maxima is reached the algorithm will halt even though ... of the state space where the evaluation fn is essentially flat. The search will conduct a random walk.
Description : 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 solution
Last Answer : A : Terminates at local optimum & Does not find optimum solution
Description : What is the name of algorithm in which 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 : Platue climbing valley
Last Answer : B : Hill-Climbing
Description : A* algorithm is based on which of the following concept? A : Best-First-Search B : Breadth-First-Search C : Depth-First –Search D : Hill climbing
Last Answer : A : Best-First-Search
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
Description : Which one of the following is not an informed search technique? (A) Hill climbing search (B) Best first search (C) A* search (D) Depth first search
Last Answer : (D) Depth first 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
Description : 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) Forest
Last Answer : b) Game Tree
Description : Define Online Search agent.
Last Answer : Agent operates by interleaving computation and action (i.e.) first it takes an action, and then it observes the environment and computes the next action.
Description : Define iterative deepening search.
Last Answer : Iterative deepening is a strategy that sidesteps the issue of choosing the best depth limit by trying all possible depth limits: first depth 0, then depth 1,then depth 2& so on.
Description : Define A* search.
Last Answer : A* search 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)
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).
Description : Define depth limited search.
Last Answer : The problem of unbounded tress can be avoided by supplying depth limit 1(i.e.) nodes at depth 1 are treated as if they have no successors. This is called Depth Limited search.
Description : Define Depth first search.
Last Answer : It expands the deepest node in the current fringe of the search tree.
Description : Define Uniform cost search.
Last Answer : Uniform cost search expands the node ‘n’ with the lowest path cost instead of expanding the shallowest node.
Description : Define Backtracking search.
Last Answer : The variant of depth first search called backtracking search. Only one successor is generated at a time rather than all successor, partially expanded node remembers which successor generate next is called Backtracking search.
Description : Define search node.
Last Answer : The root of the search tree that is the initial state of the problem is called search node.
Description : Define search tree.
Last Answer : The tree which is constructed for the search process over the state space is called search tree.
Description : What energy does a train have climbing up a hill at a steady speed?
Last Answer : The train is expending mechanical energy to be lifted higher in the earth's gravity well. As it is lifted higher, it is collecting and storing potential energy because of the fact that it is in that gravity well.
Description : A person climbing a hill bends forward in order to (a) avoid slipping (b) increase speed (c) reduce fatigue (d) increase stability
Last Answer : Ans:(d)
Description : Why do we lean forward while climbing a hill?
Last Answer : Answer: In order to keeps the vertical line passing through our centre of gravity always between our feet, which is essential to attain equilibrium or stability.
Description : When will Hill-Climbing algorithm terminate? A : Stopping criterion met B : Global Min/Max is achieved C : No neighbour has higher value D : no criteria to terminate
Last Answer : C : No neighbour has higher value
Description : How does randomized hill-climbing choose the next move each time? (A) It generates a random move from the moveset, and accepts this move. (B) It generates a random move from the whole state ... move from the whole state space, and accepts this move only if this move improves the evaluation function.
Last Answer : (C) It generates a random move from the moveset, and accepts this move only if this move improves the evaluation function.
Description : Which algorithm will work backward from the goal to solve a problem? a) Forward chaining b) Backward chaining c) Hill-climb algorithm d) None of the mentioned
Last Answer : b) Backward chaining
Description : In which of the following situations might a blind search be acceptable? a) Real life situation b) Complex game c) Small search space d) All of the mentioned
Last Answer : c) Small search space
Description : Which is true regarding BFS (Breadth First Search)? a) BFS will get trapped exploring a single path b) The entire tree so far been generated must be stored in BFS c) BFS is not guaranteed to find a solution if exists d) BFS is nothing but Binary First Search
Last Answer : b) The entire tree so far been generated must be stored in BFS
Description : What are the two major aspects which combines AI Planning problem? a) Search & Logic b) Logic & Knowledge Based Systems c) FOL & Logic d) Knowledge Based Systems
Last Answer : a) Search & Logic
Description : To eliminate the inaccuracy problem in planning problem or partial order planning problem we can use ___________________ data structure/s. a) Stacks b) Queue c) BST (Binary Search Tree) d) Planning Graphs
Last Answer : d) Planning Graphs