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Question 1 of 40 Quiz ID: q1

In the context of problem-solving agents, which of the following environments is LEAST suitable for using atomic representations and simple search algorithms?

Episodic, single agent, fully observable, deterministic, static, and discrete

Continuous, multi-agent, partially observable, stochastic, dynamic

Discrete, single agent, fully observable, deterministic, static

Episodic, single agent, fully observable, deterministic, dynamic

Question 2 of 40 Quiz ID: q2

In the Romania vacation problem, if the agent is in Timisoara and must get to Bucharest, which component of the search problem defines the set of all possible cities the agent can be in?

Initial state

Actions

State space

Goal test

Question 3 of 40 Quiz ID: q3

In the vacuum world problem with two cells, how many distinct states are there?

4

6

8

10

Question 4 of 40 Quiz ID: q4

When formulating a route-finding problem for a travel website, which factor is typically NOT part of the action cost function?

Monetary cost of the flight

Flight time

Airline brand popularity

Waiting time at the airport

Question 5 of 40 Quiz ID: q5

In a search tree, what does it mean to 'expand' a node?

To check if it is a goal state

To generate all its successor nodes by applying all applicable actions

To remove it from the frontier

To calculate its path cost

Question 6 of 40 Quiz ID: q6

Why is eliminating redundant paths important in search?

It ensures the algorithm is complete

It guarantees optimality

It prevents the algorithm from revisiting states and reduces time and space complexity

It makes the heuristic admissible

Question 7 of 40 Quiz ID: q7

Which of the following is NOT a standard criterion for evaluating search algorithms?

Completeness

Optimality

Heuristic accuracy

Time complexity

Question 8 of 40 Quiz ID: q8

For a search tree with branching factor b=10 and depth d=5, how many nodes might Breadth-First Search generate in the worst case before finding a solution at depth d?

10^5

1 + 10 + 10^2 + 10^3 + 10^4 + 10^5

10^6

5 * 10

Question 9 of 40 Quiz ID: q9

Uniform-Cost Search (UCS) is equivalent to which famous graph algorithm?

Breadth-First Search

Depth-First Search

Dijkstra's Algorithm

A* Search

Question 10 of 40 Quiz ID: q10

What is the key disadvantage of Depth-First Search (DFS) in infinite state spaces?

High time complexity

High space complexity

It is not complete

It is not optimal

Question 11 of 40 Quiz ID: q11

Iterative Deepening Depth-First Search (IDS) is often used because it:

Has the low space complexity of DFS and the completeness/optimality (for unit costs) of BFS

Is always faster than BFS

Uses a heuristic to guide the search

Is guaranteed to find the optimal solution for any cost function

Question 12 of 40 Quiz ID: q12

Bidirectional search is most effective when:

The branching factor is very high

The goal state is uniquely defined and the actions are reversible

The heuristic function is inconsistent

The path cost is calculated using a complex function

Question 13 of 40 Quiz ID: q13

Looking at the provided search algorithm comparison table, which uninformed search algorithm is complete and optimal for general action costs?

Breadth-First Search

Depth-First Search

Uniform-Cost Search

Iterative Deepening Search

Question 14 of 40 Quiz ID: q14

A heuristic function h(n) for A* search is defined as:

The actual cost to reach the goal from n

The estimated cost of the cheapest path from the state at node n to a goal

The cost incurred so far to reach node n

The average cost of all paths from n to the goal

Question 15 of 40 Quiz ID: q15

Greedy Best-First Search uses which evaluation function?

f(n) = g(n)

f(n) = h(n)

f(n) = g(n) + h(n)

f(n) = g(n) - h(n)

Question 16 of 40 Quiz ID: q16

The main problem with Greedy Best-First Search is that it:

Is computationally expensive

Requires too much memory

Is neither complete nor optimal

Cannot use heuristic functions

Question 17 of 40 Quiz ID: q17

In the A* search algorithm, what does the evaluation function f(n) = g(n) + h(n) represent?

The exact cost of the path from start to goal through n

The estimated cost of the cheapest solution path that goes through node n

The heuristic cost from n to the goal

The cost from the start node to n

Question 18 of 40 Quiz ID: q18

For A* search to be guaranteed to find an optimal solution, the heuristic function must be:

Consistent

Monotonic

Admissible

Either admissible or consistent

Question 19 of 40 Quiz ID: q19

A heuristic function h(n) is consistent if:

h(n) <= h(n') for any successor n'

h(n) <= c(n, a, n') + h(n') for every node n and its successor n' generated by action a

h(goal) = 0

h(n) is always less than the true cost

Question 20 of 40 Quiz ID: q20

The primary practical drawback of A* search is its:

Time complexity

Space complexity

Incompleteness

Inability to use heuristics

Question 21 of 40 Quiz ID: q21

In the 8-puzzle, the heuristic 'number of misplaced tiles' (h1) is:

Admissible and consistent

Admissible but not consistent

Not admissible but consistent

Neither admissible nor consistent

Question 22 of 40 Quiz ID: q22

The 'sum of Manhattan distances' heuristic (h2) for the 8-puzzle is often better than 'number of misplaced tiles' (h1) because it:

Is easier to compute

Is always a smaller number

Often provides a better (higher) estimate of the remaining cost, while still being admissible

Is not admissible but finds solutions faster

Question 23 of 40 Quiz ID: q23

A relaxed problem is generated by:

Adding restrictions to the actions

Removing restrictions from the actions

Changing the goal state

Increasing the cost of actions

Question 24 of 40 Quiz ID: q24

Pattern databases are used to:

Store the entire state space for faster lookup

Store precomputed exact solution costs for subproblems to use as admissible heuristics

Learn heuristic functions from experience

Relax the problem constraints

Question 25 of 40 Quiz ID: q25

In the route-finding problem, which heuristic is most likely to be admissible?

The road distance already traveled

The straight-line distance to the destination

The estimated time based on current traffic

The toll cost of the remaining route

Question 26 of 40 Quiz ID: q26

If an A* search using heuristic h(n) returns a solution with cost C, but the optimal cost is actually C* < C, what can be concluded about h(n)?

h(n) is admissible

h(n) is consistent

h(n) is not admissible

The search was implemented incorrectly

Question 27 of 40 Quiz ID: q27

Which data structure is most suitable for implementing the frontier in a Uniform-Cost Search?

Stack (LIFO)

Queue (FIFO)

Priority Queue (min-heap based on g(n))

Priority Queue (min-heap based on h(n))

Question 28 of 40 Quiz ID: q28

In the provided A* search example for Romania, what was the f(n) value for the initial state (Arad)?

0

366

140

366 + 140

Question 29 of 40 Quiz ID: q29

Beam Search limits the search by:

Using a depth limit

Only keeping the best k nodes at each level of the search

Using an inadmissible heuristic

Searching backwards from the goal

Question 30 of 40 Quiz ID: q30

The time complexity of A* search is:

O(b^d)

O(b^m)

O(b^(C*/ε))

Polynomial

Question 31 of 40 Quiz ID: q31

In the context of the 8-puzzle, if the 'blank' tile is included in the 'number of misplaced tiles' heuristic, what would h(start) be for the given start state?

7

8

9

0

Question 32 of 40 Quiz ID: q32

Which search algorithm would be most appropriate for a problem with a very large state space and a good admissible heuristic, where memory is the primary constraint?

Breadth-First Search

A* Search

Iterative Deepening A* Search (IDA*)

Greedy Best-First Search

Question 33 of 40 Quiz ID: q33

What is the key difference between the 'tree search' and 'graph search' versions of algorithms?

Tree search is for trees, graph search is for graphs

Graph search avoids generating redundant paths by keeping a 'closed list' of explored states

Tree search uses a heuristic, graph search does not

Graph search is always more efficient

Question 34 of 40 Quiz ID: q34

In the vacuum world, the action 'Suck' is designed to:

Move the agent to a different cell

Clean the current cell if it is dirty

Turn the agent 90 degrees

Check if the goal state is reached

Question 35 of 40 Quiz ID: q35

The process of problem formulation involves defining all of the following EXCEPT:

The initial state

The goal test

The search algorithm to be used

The actions and transition model

Question 36 of 40 Quiz ID: q36

If the step cost for every action in a problem is identical, which uninformed search algorithm is both complete and optimal?

Depth-First Search

Depth-Limited Search

Breadth-First Search

All of the above

Question 37 of 40 Quiz ID: q37

The value 'm' in search complexity analysis represents:

The maximum depth of the search tree

The depth of the shallowest solution

The branching factor

The minimum path cost

Question 38 of 40 Quiz ID: q38

A search algorithm is considered 'complete' if:

It always finds the optimal solution

It always finds a solution if one exists

It uses the minimum amount of memory

It runs in polynomial time

Question 39 of 40 Quiz ID: q39

The 'd' in the O(b^d) complexity for BFS refers to:

The depth of the search tree

The depth of the deepest node expanded

The depth of the shallowest goal node

The average depth of the tree

Question 40 of 40 Quiz ID: q40

In the context of the Romania problem, the action 'ToZerind' from state 'Arad' has an action cost of 75. What does this cost most likely represent?

Time spent driving

Distance in miles

Toll fee

Fuel consumption

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