Which rule does greedy best-first search use to choose the next state to expand?
2 Search and Problem Solving Online Quiz Questions
Use this free practice quiz with 20 questions to review 2 Search and Problem Solving, test your knowledge, and prepare for your next test or exam.
What is the name of the estimate of the remaining cost from a state to a goal, often written as h(n)?
In a state-space graph, states are represented as , while actions are represented as .
If A* uses h(n)=0 for every state, it is equivalent to uniform-cost search.
- A
True
- B
False
A puzzle solver has limited memory and can accept exploring a deep solution, even if it is not the least-cost one. Which uninformed strategy best fits these constraints?
- A
Breadth-first search
- B
Uniform-cost search
- C
Depth-first search
- D
Greedy best-first search
Select all statements that correctly describe breadth-first search (BFS).
- A
It expands the shallowest path first.
- B
It uses a first-in, first-out queue.
- C
It always expands the path with the smallest accumulated cost.
- D
It is optimal when all actions have equal cost.
On a grid where movement is allowed in four directions, a state's horizontal and vertical differences from the goal are 3 and 4 moves. What is its Manhattan-distance estimate when obstacles are ignored? Enter the count in moves.
In a search space with cycles or unbounded depth, depth-first search may follow a path indefinitely and fail to find a goal even when one exists.
- A
True
- B
False
A solution to a search problem, often called a , is a sequence of actions from the start state to a goal state.
A delivery robot's roads have different travel times, and it needs a route with the lowest total travel time. Which uninformed search strategy should it use?
- A
Breadth-first search
- B
Uniform-cost search
- C
Depth-first search
- D
Greedy best-first search
Select all statements that are correct about A* search and admissible heuristics.
- A
A* ranks states using f(n)=g(n)+h(n).
- B
A* requires h(n) to equal the true remaining cost for every state.
- C
An admissible heuristic never overestimates the true remaining cost.
- D
A* ranks states using only the estimated remaining cost, h(n).
A robot in a maze must collect several keys before reaching an exit. What information should its search state record, and why would recording only its location be insufficient?
Which statement correctly distinguishes a search tree from a state-space graph?
- A
A state can appear only once in a search tree, even if different paths reach it.
- B
The same state can appear multiple times in a search tree when reached by different paths.
- C
A state-space graph represents candidate paths, while a search tree represents actions as edges.
- D
A search tree and a state-space graph always contain exactly the same nodes and edges.
If road routes or travel costs change, a route-finding system may need to update its problem representation or cost estimates.
- A
True
- B
False
A robot explores a maze in which it must collect several objects before reaching the exit. Which state representation retains the information needed to determine both legal next actions and whether the goal has been reached?
- A
The robot’s current location only
- B
The robot’s current location and which objects it has collected
- C
The number of moves taken, regardless of what has been collected
- D
The order in which the robot first entered each corridor
Which search strategy expands the shallowest path first?
- A
Breadth-first search
- B
Depth-first search
- C
Uniform-cost search
- D
Greedy best-first search
A route-planning problem assigns different travel costs to roads, and the goal is to minimize total travel cost. Which search strategy should expand the path with the lowest accumulated cost?
- A
Breadth-first search, because it always minimizes total cost
- B
Depth-first search, because it explores one route completely
- C
Uniform-cost search, because it expands the least-cost path so far
- D
Greedy best-first search, because it expands the path with the fewest roads
A search algorithm always expands the deepest path currently on its frontier. Which description best matches its frontier behavior and a possible limitation in a state space with cycles?
- A
A first-in, first-out queue; it always finds the least-cost route
- B
A priority queue ordered by estimated remaining cost; it cannot revisit a state
- C
A priority queue ordered by accumulated cost; it always expands the shallowest path
- D
A last-in, first-out stack; it can follow a cycle indefinitely
In a search algorithm, what is the name for the collection of discovered paths that have not yet been expanded?
On a grid where movement is allowed only horizontally or vertically, a robot is at (2,3) and its goal is at (7,5). Ignoring obstacles, what is the Manhattan-distance estimate of the remaining moves?