Artificial Intelligence Areas and Concepts

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Questions and Answers

What is the probability of throwing a fair 6-sided dice twice and getting a total of 6?

1/24

1/6

5/36 (correct)

1/12

Which statement accurately describes independence in probability theory?

A is independent of B if the outcome of B influences the probability of A.

A is independent of B if A's outcome influences the probability of B.

A is independent of B if the probability of A is not influenced by B. (correct)

A is independent of B if the probability of A is influenced by B.

What does overfitting refer to in Machine Learning?

When an algorithm achieves high accuracy on training data but poorly on test data. (correct)

When the algorithm is prematurely terminated before convergence.

When a machine learning model is under-trained and unable to learn patterns.

When an algorithm is exposed to too little data to make predictions.

Which of the following tasks is typically not accomplished using unsupervised learning algorithms?

Regression Signup and view all the answers

In the context of Reinforcement Learning, what does a 'Policy' refer to?

A function that determines the action based on the current state. Signup and view all the answers

What is a Markov Decision Process?

A model defining actions, states, and rewards in an intelligent agent's environment. Signup and view all the answers

Which of the following scenarios is not considered a zero-sum game?

Participants in stock trading buying and selling assets. Signup and view all the answers

What scoring system is used in a football league when there is a draw?

Each team gets 1 point. Signup and view all the answers

What defines a mixed strategy in game theory?

A strategy chosen randomly with predefined probabilities. Signup and view all the answers

How many variables are needed to represent the dynamic state of a flying drone?

12 Signup and view all the answers

What are the main factors to consider when dealing with data from sensors?

The reliability of the sensors and data verification. Signup and view all the answers

Which option is true regarding object detection advancements?

It has evolved to include deep learning models. Signup and view all the answers

What role do computer vision applications play in the healthcare industry?

They assist in medical imagery and diagnostics. Signup and view all the answers

What is a stop word in natural language processing (NLP)?

A word commonly used but with little meaning. Signup and view all the answers

What does tokenization in NLP refer to?

The process of breaking down text into components. Signup and view all the answers

What characterizes domain-specific planning systems?

They are tailored to work for a specific application. Signup and view all the answers

What does a term in First Order Logic represent?

An object, constant, function, or variable serving as a placeholder. Signup and view all the answers

How is the encoding of an AI planning task as a state-space search problem structured?

Nodes are environment states, edges correspond to applicable actions. Signup and view all the answers

What defines a Knowledge Base in the context of AI?

A structured repository of indexed information queryable through natural language. Signup and view all the answers

Which statement correctly describes Domain-Independent planning?

It is flexible and adaptable for use across various domains. Signup and view all the answers

What type of elements do nodes and edges represent in the context of AI planning tasks?

Nodes are states and edges represent possible actions. Signup and view all the answers

Which of the following is NOT a characteristic of a term in First Order Logic?

A term consists only of complete sentences. Signup and view all the answers

How does a knowledge base differ from a traditional database in AI contexts?

A knowledge base includes reasoning capabilities for inference. Signup and view all the answers

Which of the following are main areas of Artificial Intelligence?

Reasoning, Learning, Problem Solving and Perception Signup and view all the answers

How can we differentiate between a discrete and a continuous environment?

A discrete environment has a finite number of actions and states; a continuous one has infinite possibilities. Signup and view all the answers

Which scenario represents an adversarial problem setting?

An intelligent agent playing a chess game. Signup and view all the answers

What distinguishes blind-search algorithms from informed-search algorithms?

Blind-search algorithms systematically explore all nodes, while informed-search algorithms evaluate nodes to choose the best one. Signup and view all the answers

Which statement about Local (Neighbourhood) Search algorithms is not true?

They always guarantee a globally optimal solution. Signup and view all the answers

Which option best describes the role of Machine Learning in Artificial Intelligence?

Machine Learning uses statistical methods to enable machines to improve from data. Signup and view all the answers

Which of the following is an example of a characteristic of blind-search algorithms?

They require a predefined order for exploring nodes. Signup and view all the answers

What is the main feature of informed-search algorithms compared to blind-search algorithms?

They use heuristic functions to guide their searches. Signup and view all the answers

Study Notes

Artificial Intelligence (AI) Areas

Four main areas of AI are Machine Learning, Reinforcement Learning, Problem Solving, and Vision.
Other areas include Natural Language Processing, Vision Systems, Game Theory, Robotics, Reasoning, Learning, Perception, Autonomous Vehicles, Conversational AI, Games, and Search.

Discrete vs. Continuous Environments

Discrete environments have a finite number of action choices and states.
Continuous environments have an infinite number of possible states or actions.
Discrete environments often deal with real-valued numbers in environment states.
Continuous environments typically do not have clear termination criteria (e.g., stock market).

Adversarial Problem Setting

An example of an adversarial problem setting is an intelligent agent playing chess.

Blind-search vs. Informed-search Algorithms

Blind-search algorithms systematically search all nodes in a predefined order.
Informed-search algorithms use an evaluation function to prioritize the best nodes to expand.
Informed-search algorithms often look ahead into successors.
Blind-search algorithms might not look ahead.

Local (Neighbourhood) Search Algorithms

Typically used for optimization problems.
Useful for large or continuous search spaces.
Do not guarantee finding globally optimal solutions.
Improve the current solution incrementally through small changes.

Knowledge Base

A repository of statements (sentences).
Axioms or rules derived from statements.
Indexed using natural language to allow querying.
Extracted facts from reliable sources like Wikipedia.

First Order Logic (FOL) Terms

Terms represent objects, variables, or functions of objects.
Variables are placeholders for objects.

Domain-Specific vs. Domain-Independent Planning

Domain-specific planning is tailored to a particular task or application.
Domain-independent planning uses generic algorithms applicable to various domains.
Domain-specific planning requires less memory because it doesn't need to manage as many possible scenarios in large search space.

State-Space Search Problem Encoding

Nodes represent environment states, and edges correspond to applicable actions.
Edges from an initial state node to a goal node represent a valid plan.
Nodes represent possible actions.
Edges represent effects of actions on the environment.

Probability of Throwing a Dice (2 Times, Sum = 6)

The probability of throwing a fair six-sided die twice and getting a total of 6 is 1/12.

Independence in Probability Theory

Event A is independent of event B if the probability of A is not influenced by the outcome of B, and vice versa.

Overfitting in Machine Learning

Occurs when a machine learning model is trained too long and loses predictive ability.
Often occurs due to complex models being trained on too much data.
Results in a model that performs well on training data but poorly on unseen data.

Unsupervised Learning Algorithms

Unsupervised learning algorithms typically do not use labels or pre-determined outcomes.
Anomaly detection is a common unsupervised learning example.

Algorithmic Fairness

A field of machine learning that corrects possible biases in data.
Aims to produce fairer outputs by adjusting data in machine learning algorithms.

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Description

This quiz covers key areas of Artificial Intelligence including Machine Learning, Reinforcement Learning, and Problem Solving. Additionally, it explores the differences between discrete and continuous environments, adversarial problem settings, and various search algorithms. Test your knowledge on these foundational AI concepts!