Artificial Intelligence Search Algorithms Quiz

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Questions and Answers

What is the time complexity of the depth-first search as mentioned in the content?

O(b + l)

O(b^l)

O(l^b)

O(bl) (correct)

In depth-limited search, how can a cutoff be indicated?

By returning a solution

By returning the cutoff value (correct)

By returning the maximum depth reached

By returning the standard failure value

What does the diameter of the state space refer to in the context provided?

The total number of possible sequences to investigate

The maximum number of nodes in a search tree

The maximum number of steps to reach any city from any other city (correct)

The maximum depth of any goal node

Iterative deepening depth-first search gradually increases the limit starting from which number?

0 Signup and view all the answers

What defines a problem formulation in a problem-solving approach?

Four specific items including initial state and goal test Signup and view all the answers

In the context of recursive depth-limited search, what signifies that a solution has been found?

Goal-Test passes Signup and view all the answers

Which of the following best describes a toy problem?

An illustrative problem used to compare algorithm performance Signup and view all the answers

What type of search algorithm is depth-limited search a special case of?

Depth-first search Signup and view all the answers

In the context of the Vacuum World example, what is the goal test?

Testing whether all squares are clean Signup and view all the answers

What happens when the depth limit is reached in recursive depth-limited search?

A failure is returned Signup and view all the answers

What is the primary benefit of using iterative deepening depth-first search?

It can be more efficient than depth-first search for unbounded search trees Signup and view all the answers

What does the successor function represent in problem formulation?

All possible action-state pairs from a given state Signup and view all the answers

Which statement about path cost is correct?

Path cost considers the number of actions executed Signup and view all the answers

What is a characteristic of a real-world problem?

It involves solutions that people actually care about Signup and view all the answers

In the 8-puzzle problem, what action can a tile perform?

Slide into the adjacent blank space Signup and view all the answers

For the initial state 'at Arad', what would be an example of a successor state?

Adjacent cities that can be reached via driving Signup and view all the answers

What is a defining feature of the BS Algorithm?

It is effective when generating predecessors is easy in both directions. Signup and view all the answers

What type of problem does not allow the agent to sense its environment?

Sensorless problem Signup and view all the answers

In a contingency problem, what role do percepts play?

They provide new information regarding the current state. Signup and view all the answers

What characterizes an exploration problem?

The agent has no prior knowledge of states and actions. Signup and view all the answers

In a sensorless problem, which of the following is true?

The agent cannot determine its own location. Signup and view all the answers

Which term describes a state that an agent believes it to be in?

Belief State Signup and view all the answers

What potential outcome can result from executing a 'Suck' action in a contingency problem?

It may lead to a dirty carpet due to unforeseen circumstances. Signup and view all the answers

Which search strategy is considered complete if the branching factor is finite?

Breadth-first search Signup and view all the answers

What does the hill-climbing search algorithm primarily do?

Moves in the direction of increasing value. Signup and view all the answers

What happens when a hill-climbing algorithm reaches a local maximum?

It gets stuck with no higher neighboring states. Signup and view all the answers

What are ridges in the context of hill-climbing search?

A sequence of local maxima that are difficult to navigate. Signup and view all the answers

How does a plateau affect the performance of the hill-climbing algorithm?

It can trap the algorithm in a flat local maximum. Signup and view all the answers

Why is hill-climbing sometimes referred to as a greedy local search?

It grabs the best available neighbor without planning ahead. Signup and view all the answers

What does the function HILL-CLIMBING return?

A state that is a local maximum. Signup and view all the answers

Which of the following is a potential drawback of the hill-climbing algorithm?

It can get trapped in local maxima. Signup and view all the answers

What is typically replaced at each step in the hill-climbing algorithm?

The current node with a neighbor having the highest value. Signup and view all the answers

What is the initial value of alpha during the start of the alpha-beta pruning algorithm?

$- ext{∞}$ Signup and view all the answers

When can the alpha value be updated in the pruning process?

When it is MAX's turn. Signup and view all the answers

At node B, what are the values of alpha and beta after evaluation?

alpha = $- ext{∞}$, beta = 3 Signup and view all the answers

What condition must be fulfilled for a node to be pruned?

Alpha must be greater than or equal to beta. Signup and view all the answers

What is the value of alpha at node E after its evaluation?

$5$ Signup and view all the answers

What happens to the right successor of node E after pruning?

It is not traversed. Signup and view all the answers

How does the value of alpha at node A change after evaluating node B?

It changes to 3. Signup and view all the answers

What is the final alpha value at node F after evaluating its child nodes?

$3$ Signup and view all the answers

What is the main characteristic of stochastic hill-climbing?

It randomly selects among available uphill moves. Signup and view all the answers

How does simulated annealing differ from traditional hill-climbing methods?

It accepts downhill moves based on probability. Signup and view all the answers

What is a potential drawback of a purely random walk in search algorithms?

It can become stuck at local maxima. Signup and view all the answers

In genetic algorithms, how are successor states generated?

By combining two parent states. Signup and view all the answers

What is the primary application of simulated annealing mentioned in the content?

Factory scheduling. Signup and view all the answers

Why might an algorithm using hill-climbing be considered incomplete?

It may get stuck on local maxima. Signup and view all the answers

What is the initial setup for a genetic algorithm?

A random set of k individuals. Signup and view all the answers

What does the 'badness' of a move refer to in simulated annealing?

The amount an evaluation function is worsened. Signup and view all the answers

Study Notes

Problem Solving by Search

Problem solving involves finding a sequence of actions that lead to a desirable goal.
A well-defined problem includes an initial state, an operator (successor) function to determine possible next states, a state space representing all reachable states, a path through the state space, a path cost function, and a goal test to check for a goal state.
Search is a systematic method for examining states to find the path from the initial state to the goal state.

Search Space

The search space consists of all possible states and the operators that define the connections between them.
The output of a search algorithm is a solution, or a sequence of operations that leads from the initial state to a goal state.

Problem-Solving Agents

A problem-solving agent is a goal-based agent designed for tasks by finding a sequence of actions that satisfy the goal.

Toy Problems

Vacuum World Example: An agent is operating in a two-location world. The locations can contain or not contain dirt. Goal: All squares are clean.
8-Puzzle: A 3x3 board with tiles and a blank space. Goal is to reach a goal state.
8-Queens Problem: Eight Queens on a chessboard, no queen attacking another. Goal: Place all eight queens on the board.

Real-World Problems

Route Finding: Finding the shortest path between locations. Example: Find the best route from Arad to Bucharest in Romania.
Airline Travel: Finding the best route and time for travel.
Touring Problems: Visiting each city at least once.
VLSI Layout Problem: Arranging millions of components on a chip.
Robot Navigation: Generalizing route-finding, a robot can move in a continuous space, with an infinite set of possible actions and states.

Uninformed Search Strategies

Breadth-First Search: Expands nodes in increasing depth.
Uniform-Cost Search: Expands nodes with the lowest cost first. -Both strategies are complete but not always optimal.
Depth-First Search: Explores as far as possible along each branch before backtracking.
Depth-Limited Search: Depth-first search with a pre-defined depth limit.
Iterative Deepening Search: Repeatedly applies depth-limited search with increasing depth limits.

Informed Search Strategies (Heuristic Search)

Best-First Search: Expands nodes based on an evaluation function (f(n)).
Greedy Best-First Search: Uses a heuristic function to estimate the distance to the goal (f(n) = h(n)).
A* Search: Combines cost from start to current node (g(n)) and heuristic cost to goal node (h(n)),(f(n) =g(n) + h(n)). A* Search is optimal and if the heuristic is admissible (never overestimates the true cost) then A* is optimal.
A* search is considered optimal among all algorithms because it never misses a better answer.

Local Search Algorithms

Hill Climbing: A greedy algorithm that moves towards increasingly better solutions.
Simulated Annealing: Stochastic algorithm combines greedy approach(hill-climbing) with random walk (expands any successor with probability, based on temperature).
Genetic Algorithms: Searches by generating a population of possible solutions, then selecting those with the highest evaluation function, and combines parts of the best to create new solutions.

Constraint Satisfaction Problems (CSPs)

A set of variables (X1, X2,…Xn) and a set of constraints (C1,C2,… Cm).
Each variable has a domain (possible values).
A constraint specifies allowed combinations of values for a subset of variables.
Goal: Find a complete assignment of values to variables that satisfies all constraints.
Example: Map Coloring, Cryptarithmetic, 8 Queens problem, etc

Adversarial Search

Games are competitive environments. Players' goals are in direct conflict.
Minimax Search: A game-playing algorithm.
Alpha-Beta Pruning: An optimization technique for minimax search that reduces the number of nodes evaluated.

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Description

Test your knowledge on search algorithms in artificial intelligence, focusing on key concepts such as depth-first search, iterative deepening, and problem formulation. This quiz covers essential definitions and characteristics related to problem-solving methods and their applications in AI. Ideal for students studying AI concepts.