Artificial Intelligence Areas and Concepts

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

<p>A function that determines the action based on the current state. (C)</p> Signup and view all the answers

<p>A model defining actions, states, and rewards in an intelligent agent's environment. (D)</p> Signup and view all the answers

<p>Participants in stock trading buying and selling assets. (B)</p> Signup and view all the answers

<p>Each team gets 1 point. (A)</p> Signup and view all the answers

<p>A strategy chosen randomly with predefined probabilities. (C)</p> Signup and view all the answers

<p>12 (A)</p> Signup and view all the answers

<p>The reliability of the sensors and data verification. (B)</p> Signup and view all the answers

<p>It has evolved to include deep learning models. (C)</p> Signup and view all the answers

<p>They assist in medical imagery and diagnostics. (D)</p> Signup and view all the answers

<p>A word commonly used but with little meaning. (C)</p> Signup and view all the answers

<p>The process of breaking down text into components. (B)</p> Signup and view all the answers

<p>They are tailored to work for a specific application. (C)</p> Signup and view all the answers

<p>An object, constant, function, or variable serving as a placeholder. (A)</p> Signup and view all the answers

<p>Nodes are environment states, edges correspond to applicable actions. (D)</p> Signup and view all the answers

<p>A structured repository of indexed information queryable through natural language. (D)</p> Signup and view all the answers

<p>It is flexible and adaptable for use across various domains. (B)</p> Signup and view all the answers

<p>Nodes are states and edges represent possible actions. (D)</p> Signup and view all the answers

<p>A term consists only of complete sentences. (C)</p> Signup and view all the answers

<p>A knowledge base includes reasoning capabilities for inference. (D)</p> Signup and view all the answers

<p>Reasoning, Learning, Problem Solving and Perception (C)</p> Signup and view all the answers

<p>A discrete environment has a finite number of actions and states; a continuous one has infinite possibilities. (D)</p> Signup and view all the answers

<p>An intelligent agent playing a chess game. (D)</p> Signup and view all the answers

<p>Blind-search algorithms systematically explore all nodes, while informed-search algorithms evaluate nodes to choose the best one. (D)</p> Signup and view all the answers

<p>They always guarantee a globally optimal solution. (B)</p> Signup and view all the answers

<p>Machine Learning uses statistical methods to enable machines to improve from data. (B)</p> Signup and view all the answers

<p>They require a predefined order for exploring nodes. (C)</p> Signup and view all the answers

<p>They use heuristic functions to guide their searches. (D)</p> Signup and view all the answers

A repository of information that is indexed in a way that allows for natural language queries.

A term refers to an object that can be a constant, a function of another object, or a variable representing any object that meets specific criteria.

Domain-specific planning is designed for a single application, while domain-independent planning uses general algorithms applicable across various problems.

A.I. planning tasks are often represented as a state-space search problem by using nodes to represent environment states and edges to represent actions leading to new states. A valid plan is found by tracing a path from the initial state to a goal state.

Artificial Intelligence (AI) is a branch of computer science that aims to create intelligent machines capable of performing tasks typically requiring human intelligence. It encompasses various subfields, including Machine Learning, Natural Language Processing, Computer Vision, and Robotics.

Discrete environments in AI are characterized by a finite number of actions and states. This means the agent can only choose from a fixed set of actions and the environment's state is limited to a specific set of possibilities. For example, a chess game has a finite number of moves and board configurations.

Continuous environments in AI involve an infinite number of possible actions or states. The actions and states can vary continuously, without being restricted to specific values. Examples include controlling a robot's movement in a real-world environment or predicting stock market fluctuations.

An adversarial problem setting in AI involves two or more intelligent agents competing against each other with conflicting goals. The agents strive to achieve their goals while hindering the progress of their opponents. Games such as chess, Go, or even bidding in an auction are examples of adversarial problem settings.

Blind search algorithms explore the search space systematically without using any information about the goal or heuristic guide. They examine all nodes in a predetermined order, regardless of their potential proximity to the goal. This can lead to inefficient search in large or complex spaces.

Informed search algorithms leverage domain-specific knowledge or heuristics to guide the search process. They use evaluation functions to estimate the potential usefulness of a node in reaching the goal, leading to more efficient exploration of the search space compared to blind search.

Local Search algorithms focus on finding optimal solutions within a small neighborhood of the current state. They iteratively move from one state to a neighboring state with a better evaluation, trying to find the best solution, even if it's not globally optimal. They are often used for optimization problems with large or continuous search spaces.

Local Search algorithms are often used for optimization problems as they can efficiently search through large or continuous search spaces. However, they do not guarantee finding the globally optimal solution. They can get stuck in local optima, which are solutions that are better than their immediate neighbors but not the best overall solution.

The probability of an event A is not affected by the occurrence or non-occurrence of another event B.

A machine learning model that performs well on training data but fails to generalize and performs poorly on unseen data.

An algorithm that learns from unlabeled data to discover patterns and structures.

A function that maps states to actions, guiding an agent's behavior in a reinforcement learning environment.

A mathematical framework for modeling decision-making under uncertainty. It involves states, actions, rewards, and transition probabilities.

A situation where the gain of one player is equal to the loss of another player. The total gains and losses always sum to zero.

A strategy where the player chooses actions randomly based on pre-defined probabilities. This introduces unpredictability, which can be advantageous in strategic settings.

The reliability and accuracy of sensor data, the existence of unobserved information, and the validity duration of perceived information. All of these factors are crucial for making informed decisions based on sensor data.

It requires 6 variables to represent the dynamic state of a drone: 3 for position (up/down, left/right, forward/backward) and 3 for orientation (roll, pitch, yaw).

A technique for identifying and locating objects within images or videos. Deep learning models have played a significant role in the evolution of this field.

Computer vision is being used to analyze medical images, develop diagnostic tools, and screen for potential health issues. These applications contribute to improved healthcare outcomes.

Words that are common in a language but carry little meaning. These words are often removed from text before processing to improve the accuracy of natural language processing (NLP) models.

The process of breaking down text into individual words or phrases. This is a fundamental step in most NLP tasks, as it allows the model to analyze individual units of meaning.