Introduction to Artificial Intelligence Quiz
48 Questions
0 Views

Choose a study mode

Play Quiz
Study Flashcards
Spaced Repetition
Chat to lesson

Podcast

Play an AI-generated podcast conversation about this lesson

Questions and Answers

Which technique helps reduce the horizon effect in game AI?

  • Focusing on terminal states only
  • Increasing the branching factor
  • Limiting the search depth
  • Extending the search at critical points using quiescence search (correct)

    • What is a key feature of AlphaGo Zero's learning approach?

  • Utilizing a predefined set of strategies from past games
  • Training exclusively with human expert data
  • Relying solely on heuristic evaluations
  • Self-play with reinforcement learning, without human data (correct)

    • Transposition tables are particularly beneficial in which scenario?

  • Games with straightforward move decisions
  • Games without randomness
  • Games played between only two participants
  • Games with many repeated states reached through different sequences of moves (correct)

    • In Monte Carlo Tree Search (MCTS), what does exploitation refer to?

    <p>Using moves known to be good based on previous simulations (D)</p> Signup and view all the answers

    Which field demonstrates the use of adversarial search beyond games?

    <p>Cybersecurity, where AI must counteract attackers (A)</p> Signup and view all the answers

    What AI system is recognized for its exceptional performance in chess?

    <p>Deep Blue (C)</p> Signup and view all the answers

    Which definition best describes a discrete random variable?

    <p>A variable with a finite number of distinct values (D)</p> Signup and view all the answers

    What term is used for a probability distribution applicable to continuous random variables?

    <p>Probability Density Function (PDF) (A)</p> Signup and view all the answers

    In a Fork structure, what is the relationship between A and C?

    <p>They are conditionally independent given B. (D)</p> Signup and view all the answers

    Which technique is commonly used in approximate inference methods?

    <p>Sampling methods like Monte Carlo. (D)</p> Signup and view all the answers

    What is the primary feature of Bayesian Parameter Learning?

    <p>It updates beliefs based on both data and prior knowledge. (A)</p> Signup and view all the answers

    The Bayesian Network structure A → B ← C is known as what?

    <p>Collider. (C)</p> Signup and view all the answers

    What do Bayesian Networks primarily allow for?

    <p>Probabilistic reasoning with conditional dependencies. (C)</p> Signup and view all the answers

    What is the main purpose of d-Separation in Bayesian Networks?

    <p>To determine which nodes are conditionally independent. (A)</p> Signup and view all the answers

    What capability does a Bayesian Network provide?

    <p>Model uncertain events and their dependencies. (C)</p> Signup and view all the answers

    In the context of Bayesian Networks, what is MAP estimation used for?

    <p>Finding parameter values that maximize the posterior distribution. (C)</p> Signup and view all the answers

    Which characteristics are associated with planning agents?

    <p>They use a model of the environment to predict future states. (A), They generate sequences of actions to achieve a goal. (C)</p> Signup and view all the answers

    Which methods are classified as uninformed search methods?

    <p>Uniform-cost search (A), Breadth-first search (B), Depth-first search (C)</p> Signup and view all the answers

    A* search employs which criteria to select the next node for exploration?

    <p>Heuristic estimate to the goal (h(n)) (B), Total path cost (g(n)) (C)</p> Signup and view all the answers

    Which statements accurately describe reflex agents?

    <p>They act based on the current percept. (B), They rely on condition-action rules to make decisions. (D)</p> Signup and view all the answers

    Which statement correctly defines characteristics of problem-solving agents?

    <p>They consider the consequences of their actions. (A)</p> Signup and view all the answers

    What is a defining feature of reflex agents?

    <p>They respond based solely on their current input. (D)</p> Signup and view all the answers

    Which of the following search strategies can guarantee an optimal solution if the path cost is non-negative?

    <p>Uniform-cost search (C)</p> Signup and view all the answers

    In the context of planning agents, which of the following best describes their functionality?

    <p>They predict future outcomes based on current actions. (A), They utilize memory of past actions to inform future decisions. (C)</p> Signup and view all the answers

    What does it mean if A and C are independent given B in a Bayesian Network?

    <p>Observing B gives all necessary information about A and C (B)</p> Signup and view all the answers

    Which of the following methods is categorized as exact inference?

    <p>Variable Elimination (D)</p> Signup and view all the answers

    What is true about Inference by Enumeration?

    <p>Accurate but computationally expensive (C)</p> Signup and view all the answers

    How does Variable Elimination improve efficiency in Bayesian Networks?

    <p>Eliminating variables systematically to simplify calculations (B)</p> Signup and view all the answers

    What does the Bayesian Network structure A → B → C imply?

    <p>A directly affects B, which then affects C (D)</p> Signup and view all the answers

    Which description accurately represents Maximum a Posteriori (MAP) Estimation?

    <p>Both observed data and prior beliefs (C)</p> Signup and view all the answers

    When is approximate inference typically employed in Bayesian Networks?

    <p>When the network is too large for exact inference (A)</p> Signup and view all the answers

    In Bayesian learning, what distinguishes it from Maximum Likelihood Parameter Learning?

    <p>Bayesian learning incorporates prior knowledge (C)</p> Signup and view all the answers

    Which expression is correct for independent events?

    <p>P (A | B) = P (A) (C)</p> Signup and view all the answers

    What is the purpose of Bayes’ Rule?

    <p>Update prior beliefs with new evidence (B)</p> Signup and view all the answers

    How is probability defined from a frequentist perspective?

    <p>Probability is the long-run frequency of events (C)</p> Signup and view all the answers

    What does Kolmogorov’s second axiom assert?

    <p>The probability of mutually exclusive events is additive (A)</p> Signup and view all the answers

    In probability, what is the sum of all possible probabilities in a distribution equal to?

    <p>1 (B)</p> Signup and view all the answers

    What action does the Principle of Maximum Expected Utility suggest agents should take?

    <p>Choose actions with the highest expected utility (D)</p> Signup and view all the answers

    What does a conditional distribution represent?

    <p>The distribution of one variable given the value of another (A)</p> Signup and view all the answers

    Which statement best characterizes a Naive Bayes Model?

    <p>It assumes all features are conditionally independent given the class (C)</p> Signup and view all the answers

    What does a prior probability represent?

    <p>The initial belief before any evidence is observed (C)</p> Signup and view all the answers

    Why is a probability density function (PDF) typically used?

    <p>Define probabilities for continuous random variables (B)</p> Signup and view all the answers

    What is a significant advantage of conditional independence in probabilistic models?

    <p>It reduces the number of parameters needed (A)</p> Signup and view all the answers

    What is a key application of the chain rule in probability?

    <p>Simplifying joint probabilities into conditional probabilities (B)</p> Signup and view all the answers

    Which scenario exemplifies Bayesian inference?

    <p>Updating the likelihood of rain given new weather data (A)</p> Signup and view all the answers

    What foundational role does Bayes’ Rule play in artificial intelligence?

    <p>Enabling updating beliefs based on evidence (A)</p> Signup and view all the answers

    In a Bayesian Network, what do the nodes represent?

    <p>Random variables (D)</p> Signup and view all the answers

    Signup and view all the answers

    Study Notes

    Introduction to Artificial Intelligence

    • Course title: Introduction to Artificial Intelligence
    • Instructor: Pouria Katouzian
    • Date: October 2024

    Contents

    • Intelligent Agents: Includes multiple-choice questions (MCQs) on characteristics of planning agents, uninformed search methods (Depth-first, Breadth-first, Uniform-cost), and reflex agents. Also includes MCQs on problem-solving agents.
    • Games and Adversarial Search: Covers introduction to adversarial search, types of uninformed search methods (uniform-cost, depth-first, breadth-first), and characteristics of reflex agents. Includes MCQs on games and adversarial search, focusing on deterministic games with perfect information, terminal states in games, minimax algorithm, and the time complexity of minimax.
    • Solving Problems by Searching: Includes MCQs on discrete random variables, probability distribution types (PMF, PDF, CDF), conditional probability, independent events, and Bayesian reasoning.
    • Probabilistic Reasoning: Detailed explanation and examples, including MCQs about Kolmogorov's axioms for probability, marginal distributions, the probability distribution sum, conditional independence, and the principle of maximum expected utility.
    • Reasoning Over Time in Artificial Intelligence: Focuses on MCQs about Markov Models, its assumptions (future only depends on the current state), temporal reasoning inference tasks, prediction, and filtering. Also covers transition and sensor models within Markov processes.
    • Machine Learning and Neural Networks: Introduces reinforcement learning as a machine learning paradigm, key concepts (exploration, exploitation, rewards, reinforcement), and the main goal of maximizing cumulative rewards.
    • Reinforcement Learning (RL): Detailed explanation of reinforcement learning (RL) concepts through MCQs about the agent's learning process from rewards and exploration/exploitation trade-off.
    • Detailed Explanation of Reinforcement Learning Topics: Expands on topics covered in prior sections, including Markov Decision Processes (MDPs), Q-learning, Value Iteration, Policy Iteration, and temporal-difference (TD) methods. Includes a detailed explanation of the types of reinforcement learning, advantages of certain methods, and their respective use cases.
    • Multiple-Choice Questions (MCQs) on Reinforcement Learning: Includes comprehensive MCQs covering various reinforcement learning concepts.

    Studying That Suits You

    Use AI to generate personalized quizzes and flashcards to suit your learning preferences.

    Quiz Team

    Related Documents

    Introduction to Artificial Intelligence PDF

    Description

    Test your knowledge on intelligent agents, adversarial search, and problem-solving methods in artificial intelligence. This quiz includes multiple-choice questions covering key concepts and algorithms essential for understanding AI. Prepare to explore the fundamentals that drive intelligent systems and decision-making processes.

    More Like This

    Use Quizgecko on...
    Browser
    Open
    Browser
    Browser