Session 3

Question 1

What is the definition of a search strategy?

Answer 1

A search strategy is a method used by problem-solving agents to explore the state space and find a solution to a given problem.

Question 2

What is the purpose of a search strategy?

Answer 2

The purpose of a search strategy is to systematically traverse the state space to reach the goal state from the initial state.

Question 3

What are the two main types of search strategies?

Answer 3

The two main types of search strategies are uninformed search and informed search.

Question 4

What is an uninformed search strategy?

Answer 4

An uninformed search strategy is a method that does not use additional information about the goal state beyond the problem definition.

Question 5

What is an informed search strategy?

Answer 5

An informed search strategy is a method that uses heuristic information to estimate the cost of reaching the goal state.

Question 6

What is Breadth-First Search (BFS)?

Answer 6

BFS is an uninformed search strategy that explores all nodes at the current depth before moving to the next depth.

Question 7

What is Depth-First Search (DFS)?

Answer 7

DFS is an uninformed search strategy that explores as far as possible along a branch before backtracking.

Question 8

What is Uniform Cost Search (UCS)?

Answer 8

UCS is an uninformed search strategy that expands the node with the lowest path cost.

Question 9

What is Iterative Deepening Search (IDS)?

Answer 9

IDS is an uninformed search strategy that combines the space efficiency of DFS with the completeness of BFS by performing DFS with increasing depth limits.

Question 10

What is a heuristic function?

Answer 10

A heuristic function is an estimate of the cost to reach the goal from a given state.

Question 11

What is the key advantage of A* search?

Answer 11

A* search guarantees the optimal solution if the heuristic is admissible (never overestimates the cost).

Question 12

What is the main limitation of DFS?

Answer 12

DFS is not guaranteed to find the optimal solution and can get stuck in infinite branches.

Question 13

What is the main advantage of BFS?

Answer 13

BFS is complete and optimal for unweighted graphs.

Question 14

What is the main limitation of UCS?

Answer 14

UCS can be slow if many nodes have similar costs.

Question 15

What is the main advantage of IDS?

Answer 15

IDS is complete and uses less memory than BFS.

Question 16

What is the main limitation of Greedy Best-First Search?

Answer 16

Greedy Best-First Search does not guarantee the optimal solution.

Question 17

What is the purpose of a state space representation?

Answer 17

State space representation is used to model all possible states and transitions in a problem.

Question 18

What is a search tree?

Answer 18

A search tree is a tree representation of the state space where nodes represent states and edges represent actions.

Question 19

What is the role of feedback in learning agents?

Answer 19

Feedback helps learning agents improve their performance by evaluating actions and guiding the learning process.

Question 20

What are the four components of a learning agent?

Answer 20

The four components of a learning agent are the performance element, learning element, critic, and problem generator.

Question 21

What is supervised learning?

Answer 21

Supervised learning is a method where the agent learns from labeled data with known outcomes.

Question 22

What is unsupervised learning?

Answer 22

Unsupervised learning is a method where the agent learns patterns and structures from unlabeled data.

Question 23

What is reinforcement learning?

Answer 23

Reinforcement learning is a method where the agent learns by interacting with the environment and receiving rewards or penalties.

Question 24

What is the role of the critic in a learning agent?

Answer 24

The critic evaluates the agent’s actions and provides feedback to guide learning.

Question 25

What is the role of the problem generator in a learning agent?

Answer 25

The problem generator suggests exploratory actions to improve the agent's learning.

Question 26

What is the main advantage of learning agents?

Answer 26

Learning agents can adapt to dynamic and uncertain environments.

Question 27

What is the main challenge of learning agents?

Answer 27

Learning agents require large amounts of data and computational resources.

Question 28

What is the Grid World problem?

Answer 28

The Grid World problem is a two-dimensional grid where agents move between cells to achieve a goal.

Question 29

What is the Sliding-Tile Puzzle?

Answer 29

The Sliding-Tile Puzzle involves moving tiles into an empty space to achieve a specific configuration.

Question 30

What is the Sokoban Puzzle?

Answer 30

The Sokoban Puzzle is a grid-based puzzle where the agent pushes boxes to designated storage locations.

Question 31

What is the Traveling Salesperson Problem (TSP)?

Answer 31

TSP is a problem where the agent must visit all cities and return to the starting point while minimizing travel cost.

Question 32

What is the main challenge of TSP?

Answer 32

TSP is an NP-hard problem with exponential complexity.

Question 33

What is the purpose of route-finding problems?

Answer 33

Route-finding problems aim to find the shortest or optimal path between two locations.

Question 34

What is the role of a utility function in utility-based agents?

Answer 34

A utility function evaluates the desirability of different states to help the agent optimize its actions.

Question 35

What is a simple reflex agent?

Answer 35

A simple reflex agent selects actions based solely on the current percept, ignoring percept history.

Question 36

What is a model-based agent?

Answer 36

A model-based agent maintains an internal model of the world to handle partially observable environments.

Question 37

What is a goal-based agent?

Answer 37

A goal-based agent makes decisions based on goals it needs to achieve.

Question 38

What is a utility-based agent?

Answer 38

A utility-based agent uses a utility function to evaluate and optimize its actions.

Question 39

What is the main limitation of simple reflex agents?

Answer 39

Simple reflex agents cannot handle partially observable environments or learn from experiences.

Question 40

What is the main advantage of model-based agents?

Answer 40

Model-based agents can handle partially observable environments by maintaining an internal state.

Question 41

What is the main limitation of goal-based agents?

Answer 41

Goal-based agents may require significant computational resources for complex goals.

Question 42

What is the main advantage of utility-based agents?

Answer 42

Utility-based agents can handle trade-offs between conflicting goals and optimize performance.

Question 43

What is the main advantage of learning agents over other types?

Answer 43

Learning agents can improve their performance over time by learning from feedback and experiences.

Question 44

What is the Vacuum Cleaner Problem?

Answer 44

The Vacuum Cleaner Problem involves an agent cleaning dirt in a grid-based environment.

Question 45

What is the purpose of automatic assembly sequencing?

Answer 45

Automatic assembly sequencing determines the order of assembling parts to create a product.

Question 46

What is the main advantage of A* search over UCS?

Answer 46

A* search combines path cost and heuristic value to find the optimal solution more efficiently.

Question 47

What is the main limitation of Greedy Best-First Search compared to A*?

Answer 47

Greedy Best-First Search does not guarantee the optimal solution, while A* does.

Question 48

What is the role of exploration in reinforcement learning?

Answer 48

Exploration allows the agent to try new actions to discover better strategies.

Question 49

What is the role of exploitation in reinforcement learning?

Answer 49

Exploitation involves using known strategies to maximize rewards.

Question 50

What is the main challenge in balancing exploration and exploitation?

Answer 50

Balancing exploration and exploitation is challenging because too much exploration can waste resources, while too much exploitation can prevent discovering better strategies.