Questions

  • What is the main goal of Artificial Intelligence (AI)?

    • A) To mimic human intelligence and behavior

    • B) To build machines with unlimited power

    • C) To create systems that work independently from human data

    • D) To predict the future with perfect accuracy
      Answer: A

  • What is Machine Learning (ML) primarily focused on?

    • A) Programming robots to walk

    • B) Developing algorithms for pattern recognition and predictions

    • C) Designing complex hardware for AI

    • D) Automating manual tasks without data
      Answer: B

  • Which of the following is NOT a type of Machine Learning?

    • A) Supervised Learning

    • B) Unsupervised Learning

    • C) Reinforcement Learning

    • D) Multimodal Learning
      Answer: D

  • In the PEAS framework, what does 'P' stand for?

    • A) Performance Measure

    • B) Power Level

    • C) Predictive Model

    • D) Probability Factor
      Answer: A

  • In PEAS, what role do 'Actuators' play?

    • A) Perceive the environment

    • B) Measure the agent's success

    • C) Enable the agent to take actions

    • D) Set the agent’s environment
      Answer: C

  • Which is an example of a 'stochastic game'?

    • A) Chess

    • B) Tic-Tac-Toe

    • C) Poker

    • D) Sudoku
      Answer: C

  • What does the 'E' in PEAS represent?

    • A) Energy

    • B) Efficiency

    • C) Environment

    • D) Evaluation
      Answer: C

  • In adversarial search, the 'Minimizer' aims to:

    • A) Maximize their score

    • B) Minimize their opponent’s score

    • C) Improve search speed

    • D) Eliminate game randomness
      Answer: B

  • Which algorithm is commonly used in adversarial search?

    • A) K-Nearest Neighbors

    • B) Minimax

    • C) Random Forest

    • D) K-Means
      Answer: B

  • Alpha-Beta Pruning is used to:

    • A) Generate new data for training

    • B) Optimize Minimax by skipping unnecessary evaluations

    • C) Measure prediction accuracy

    • D) Visualize decision trees
      Answer: B

  • Which type of game has complete information available to all players?

    • A) Perfect Information Game

    • B) Imperfect Information Game

    • C) Stochastic Game

    • D) Multiplayer Game
      Answer: A

  • Which of these games is a zero-sum game?

    • A) Poker

    • B) Chess

    • C) Monopoly

    • D) Diplomacy
      Answer: B

  • What is a primary component of a search problem in AI?

    • A) User feedback

    • B) Random state generation

    • C) Initial state, goal state, and actions

    • D) Reward and penalty system
      Answer: C

  • What distinguishes uninformed search algorithms?

    • A) They rely on prior knowledge of the goal

    • B) They perform random searches

    • C) They use no additional information on the goal other than the problem definition

    • D) They are only applicable in deterministic environments
      Answer: C

  • Which search algorithm explores all nodes at the present level before moving to the next?

    • A) Depth-First Search

    • B) Uniform Cost Search

    • C) Breadth-First Search

    • D) Minimax
      Answer: C

  • A Markov Decision Process (MDP) is mainly used in:

    • A) Supervised learning

    • B) Decision-making with uncertainty

    • C) Data preprocessing

    • D) Model evaluation
      Answer: B

  • In MDP, what does 'R' represent?

    • A) Rewards

    • B) Resources

    • C) Result

    • D) Regression
      Answer: A

  • A Grid World is an example of:

    • A) Supervised learning environment

    • B) Reinforcement learning environment

    • C) Unsupervised learning environment

    • D) None of the above
      Answer: B

  • Which component in MDP represents a sequence of actions?

    • A) Rewards

    • B) Policy

    • C) Goal

    • D) Path Cost
      Answer: B

  • A deterministic grid world means:

    • A) Movements are probabilistic

    • B) Movements are predictable

    • C) Rewards are randomized

    • D) Actions are unpredictable
      Answer: B


  • Define Artificial Intelligence (AI).

    • AI is the field of computer science that focuses on creating systems or machines capable of performing tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.

  • What is the purpose of Machine Learning (ML)?

    • The purpose of ML is to enable machines to learn from data and improve their performance on specific tasks over time without being explicitly programmed.

  • List the three main types of Machine Learning.

    • Supervised Learning, Unsupervised Learning, and Reinforcement Learning.

  • What is the PEAS framework used for in AI?

    • The PEAS framework is used to specify the Performance measure, Environment, Actuators, and Sensors of an AI agent, helping to define its goals and operating conditions.

  • Describe the purpose of the Performance Measure in PEAS.

    • The Performance Measure defines the criteria for success and measures how well an agent is achieving its goals.

  • Give an example of an actuator in a self-driving car.

    • An example of an actuator in a self-driving car is the steering wheel control mechanism, which adjusts the car's direction.

  • What is adversarial search?

    • Adversarial search is a search technique used in competitive environments, such as games, where agents (players) compete against each other to achieve their goals.

  • Name the two roles in the Minimax algorithm.

    • The two roles are Maximizer and Minimizer.

  • Why is Alpha-Beta Pruning useful?

    • Alpha-Beta Pruning is useful because it reduces the number of nodes evaluated in the Minimax algorithm, making it faster and more efficient.

  • Explain the difference between deterministic and stochastic games.

    • Deterministic games have no element of chance, while stochastic games include randomness, such as rolling dice or drawing cards.

  • What are perfect information games?

    • Perfect information games are games where all players have access to all information about the game state (e.g., Chess).

  • Give an example of a zero-sum game.

    • An example of a zero-sum game is Tic-Tac-Toe, where one player's gain is the other player's loss.

  • What is a search problem?

    • A search problem is a problem where the solution involves finding a path from an initial state to a goal state.

  • What is the initial state in a search problem?

    • The initial state is the starting point or configuration of the agent before any actions are taken.

  • What is meant by uninformed search?

    • Uninformed search refers to search strategies that have no additional information about the goal other than the problem definition.

  • List three uninformed search algorithms.

    • Breadth-First Search (BFS), Depth-First Search (DFS), and Uniform Cost Search.

  • What is a fringe in the context of search algorithms?

    • The fringe is the set of nodes that have been generated but not yet explored.

  • Define a Markov Decision Process (MDP).

    • An MDP is a mathematical framework used to model decision-making in situations where outcomes are partly random and partly under the control of an agent.

  • Name the components of an MDP.

    • The components are States, Actions, Transition Model, Rewards, and Policy.

  • Explain the purpose of rewards in MDP.

    • Rewards provide feedback to the agent on the desirability of reaching a particular state, helping to guide it toward achieving the goal.

  • What is a grid world in reinforcement learning?

    • A grid world is a simple environment represented by a grid where an agent moves between cells to achieve a goal.

  • How does a deterministic grid world differ from a stochastic one?

    • In a deterministic grid world, the agent's actions have predictable outcomes. In a stochastic grid world, actions may have uncertain or random outcomes.

  • What is a transition model in search problems?

    • The transition model describes how actions taken by an agent lead to changes in the state.

  • Differentiate between finite and infinite horizon in MDPs.

    • A finite horizon MDP has a fixed time frame for decision-making, while an infinite horizon MDP has an indefinite or infinite time frame.

  • Describe the goal of Alpha-Beta Pruning.

    • The goal of Alpha-Beta Pruning is to reduce the number of nodes explored in the Minimax algorithm, improving search efficiency without affecting the outcome.

  • What is the role of a policy in MDP?

    • A policy is a strategy or rule that defines the actions an agent should take in each state to maximize its expected reward.

  • Explain the purpose of path cost in search problems.

    • The path cost is the total cost of the path from the initial state to a given state, used to evaluate the efficiency of a solution.

  • How does Breadth-First Search (BFS) differ from Depth-First Search (DFS)?

    • BFS explores all nodes at the current depth before moving deeper, while DFS explores as far as possible along one path before backtracking.

  • What is a goal state in the context of search problems?

    • A goal state is the state the agent aims to reach, satisfying the problem's objective.

  • Explain the term "state space."

    • The state space is the set of all possible states that can be reached from the initial state in a search problem.