Artificial Intelligence Overview Quiz

<p>A computer's ability to mimic human conversation (D)</p> Signup and view all the answers

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

<p>Problems that are too complex for conventional algorithms (B)</p> Signup and view all the answers

<p>Manage lack of observability (A)</p> Signup and view all the answers

<p>Tasks involving reward against adversaries (B)</p> Signup and view all the answers

<p>Narrow AI specializes in a specific domain. (A)</p> Signup and view all the answers

<p>Control of vehicles. (A)</p> Signup and view all the answers

<p>Industrial Automation. (A)</p> Signup and view all the answers

<p>To assist in genomic diagnostics. (A)</p> Signup and view all the answers

<p>Providing companionship and assistance. (C)</p> Signup and view all the answers

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

<p>Intelligent Control. (D)</p> Signup and view all the answers

<p>Problem solving in a specific domain. (A)</p> Signup and view all the answers

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

<p>It always results in the same outcome. (A)</p> Signup and view all the answers

<p>Throwing a dice in backgammon. (C)</p> Signup and view all the answers

<p>Finite number of action choices. (B)</p> Signup and view all the answers

<p>The future actions of competing agents. (A)</p> Signup and view all the answers

<p>Maintaining historical information. (D)</p> Signup and view all the answers

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

<p>A temperature control system. (A)</p> Signup and view all the answers

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

<p>Goal-Based Agent (D)</p> Signup and view all the answers

<p>Predicting the opponent's actions (A)</p> Signup and view all the answers

<p>Sensors perceive the environment by collecting data. (B)</p> Signup and view all the answers

<p>Personal Shopping (D)</p> Signup and view all the answers

<p>Maintaining an internal state based on history (D)</p> Signup and view all the answers

<p>The complete history of all sensor data received. (C)</p> Signup and view all the answers

<p>Acts without considering past percepts (B)</p> Signup and view all the answers

<p>A robot exploring underwater for oceanic research. (C)</p> Signup and view all the answers

<p>An agent trying to predict the weather (A)</p> Signup and view all the answers

<p>Maintaining a complete history is not always feasible. (B)</p> Signup and view all the answers

<p>Through planning relative to its future goals (B)</p> Signup and view all the answers

<p>It directs its activity towards achieving goals using sensors. (B)</p> Signup and view all the answers

<p>Involves competition against other agents (C)</p> Signup and view all the answers

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

<p>A comprehensive model of the environment (A)</p> Signup and view all the answers

<p>To evaluate and select actions that maximize some utility. (B)</p> Signup and view all the answers

<p>To provide feedback on performance to improve learning. (B)</p> Signup and view all the answers

<p>Optimal deterministic actions. (D)</p> Signup and view all the answers

<p>By exploring new knowledge and receiving feedback. (A)</p> Signup and view all the answers

<p>It may include stochastic outcomes from actions. (D)</p> Signup and view all the answers

<p>To modify its actions based on learned knowledge. (C)</p> Signup and view all the answers

<p>It creates new scenarios for the agent to learn from. (B)</p> Signup and view all the answers

<p>Some aspects of the environment are hidden from the agent. (B)</p> Signup and view all the answers

The ability to reason logically, infer missing information, understand concepts, solve problems, learn from experience, make decisions, plan, be creative, and experience consciousness and self-awareness.

A field of computer science that focuses on creating systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.

A test proposed by Alan Turing in 1950 to determine whether a machine can exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.

The ability of intelligent agents to handle ambiguous, noisy, and incomplete information.

The ability of intelligent agents to make decisions and act in situations where they don't have complete information.

The ability of intelligent agents to deal with situations where outcomes are uncertain.

The ability of intelligent agents to learn from new experiences and adapt to changing environments.

The ability of intelligent agents to adjust their behavior to adapt to dynamic environments.

A type of AI that excels in a specific area or task, like playing chess or recognizing objects in images.

A type of AI with the ability to learn and apply intelligence to any problem, regardless of the domain. It's like a generalist with vast knowledge and skills.

A fundamental area of AI research that focuses on how machines can reason logically and solve problems. It involves using knowledge representation, inference, and rules to draw conclusions.

A crucial aspect of AI research that focuses on how machines can learn from data and improve their performance over time. This encompasses different techniques like supervised, unsupervised, and reinforcement learning.

A part of AI research that focuses on how machines can perceive and interpret the world around them through sensory input, like images, sounds, and text.

A core area of AI research that explores strategies and approaches to find the best solution to a given problem, taking into account factors like efficiency, optimality, and constraints.

The application of AI to control and automate robotic systems, ranging from industrial robots in factories to autonomous vehicles like self-driving cars.

The use of AI to improve efficiency and performance in various industries, including manufacturing, power plants, and supply chain management. It aims to create smart systems that optimize resources, detect errors, and enhance safety.

Refers to any entity that uses sensors to observe its environment and actuators to interact with it.

Involves the continuous loop of an agent perceiving its environment with sensors, processing information, and then taking actions using actuators.

The complete recorded history of all sensory data an agent has received.

Combat AI systems that can simulate adversaries in games, providing realistic and challenging opponents.

A non-player character (NPC) in a game, often controlled by AI, that interacts with the player and responds to the game world

AI applications that use sensors to understand the environment and user intentions for both physical and virtual reality experiences.

AI applications that assist humans in various tasks like scheduling, reminders, and information retrieval.

Includes the use of AI for various applications like personalized learning, automated grading, and intelligent tutoring systems.

A rational agent behaves optimally based on its knowledge, environment, and goals. To achieve this, it considers the performance measure (what success looks like), its prior knowledge of the environment, the possible actions it can take, and the information it gathers from its sensors (percepts).

An environment where not all information needed to make an optimal decision is accessible to the agent's sensors. The agent cannot know everything about the situation.

A poker playing agent only has visibility of its own cards, making the environment partially observable.

An environment where the result of an action is not always predictable. The same action can have different outcomes.

Rolling a dice in a backgammon game has unpredictable outcomes making the environment stochastic.

An environment with a finite number of states and actions. The agent has a limited number of choices it can make.

An environment where states and actions can be anything within a range. The agent has many choices and can act in a continuous manner.

An agent's environment is considered adversarial if the agent is competing against other agents, such as humans, to achieve its goals.

An agent's environment is considered benign if it is not actively working against the agent's goals. The agent can focus on achieving its objectives without needing to counter other agents.

A simple reflex agent only considers the current state of the environment, ignoring past information. It acts based on direct input from sensors and predefined rules.

A model-based reflex agent improves on simple reflex agents by maintaining an internal state that represents the current state of the environment. This state is built using the agent's understanding of how the environment changes and how its actions affect the environment.

A goal-based agent focuses on achieving a specific goal, planning ahead to choose actions that will ultimately lead to that goal. This means it considers the future state of the environment and evaluates actions based on their potential to approach the goal.

A state is a complete description of the relevant aspects of the environment at a given moment in time. It is a snapshot of what is happening in the world, containing all important information about the agent's current situation.

Actions are the choices that an intelligent agent can take in response to its perception of the environment. Depending on the type of environment, an agent's actions can have various effects on the world.

An agent that considers future consequences of its actions and chooses the ones that maximize a predefined utility function. This function measures the desirability of different states.

The actions an agent can take to change the world. These are often physical actions but can also be things like sending messages or requesting data.

The agent's perception of the current state of the world. They provide information about the environment.

The agent's goal is to achieve a state or outcome that is beneficial or desirable.

The environment the agent operates in. This can be real or simulated, and can include other agents or obstacles.

The way the world changes over time, independent of the agent's actions. These changes can be deterministic or stochastic.

The agent will choose actions that are likely to result in a favorable outcome, based on its understanding of how the world evolves. The outcome might be uncertain due to noise, incomplete information, or complex dynamics.