AI Agents and Accessibility Quiz

<p>Current percepts only. (D)</p> Signup and view all the answers

<p>From perception sequence to action. (B)</p> Signup and view all the answers

<p>Model-based reflex agents. (A)</p> Signup and view all the answers

<p>Maximization of user-defined utilities. (B)</p> Signup and view all the answers

<p>Socially-aware agents. (C)</p> Signup and view all the answers

<p>Only one agent is involved in the environment. (A)</p> Signup and view all the answers

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

<p>Actions and percepts are finite in number. (A)</p> Signup and view all the answers

<p>The agent knows the results for all actions. (D)</p> Signup and view all the answers

<p>It has multiple agents that may interact. (B)</p> Signup and view all the answers

<p>Self-driving car (B)</p> Signup and view all the answers

<p>The agent does not need to monitor the environment constantly. (B)</p> Signup and view all the answers

<p>Agents must acquire knowledge about the environment. (A)</p> Signup and view all the answers

<p>To keep track of the world by updating its internal state (B)</p> Signup and view all the answers

<p>They require specific goal information to determine actions (B)</p> Signup and view all the answers

<p>The reliability, safety, and cost-effectiveness of the sequence (A)</p> Signup and view all the answers

<p>It provides feedback on the agent’s performance against standards (A)</p> Signup and view all the answers

<p>It adapts its actions based on past experiences (D)</p> Signup and view all the answers

<p>By combining current perceptions with old internal states (A)</p> Signup and view all the answers

<p>Actions are aligned with both the current state and desired goals (C)</p> Signup and view all the answers

<p>To choose the option that maximizes overall quality of behavior (D)</p> Signup and view all the answers

<p>The solution with the lowest path cost (A)</p> Signup and view all the answers

<p>To test various problem solving techniques (A)</p> Signup and view all the answers

<p>8 states (D)</p> Signup and view all the answers

<p>Real-world problems do not rely on descriptions. (B)</p> Signup and view all the answers

<p>Walls or other impassable obstructions (B)</p> Signup and view all the answers

<p>2-dimensional rectangular array of square cells (A)</p> Signup and view all the answers

<p>Suck up dirt from the floor (C)</p> Signup and view all the answers

<p>The various configurations of the agent and dirt in the cells (B)</p> Signup and view all the answers

<p>To replicate human intelligence in machines. (C)</p> Signup and view all the answers

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

<p>Human-made and designed to mimic natural processes. (D)</p> Signup and view all the answers

<p>To perform tasks efficiently and achieve specific goals. (C)</p> Signup and view all the answers

<p>One refers to natural abilities, while the other refers to machine-based abilities. (C)</p> Signup and view all the answers

<p>The ability to learn and improve over time. (B)</p> Signup and view all the answers

<p>The fundamental concepts and components of AI. (B)</p> Signup and view all the answers

<p>The capability to achieve goals efficiently and effectively. (A)</p> Signup and view all the answers

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

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

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

<p>Linear vs Non-linear (D)</p> Signup and view all the answers

<p>The agent has no information about the environment's rules or dynamics. (B)</p> Signup and view all the answers

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

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

<p>The agent can access all parts of the environment. (C)</p> Signup and view all the answers

An environment where an agent can sense the complete state at all times.

An environment where an agent cannot access the complete state at all times.

An environment where the next state is completely determined by current state and action.

An environment where the next state is uncertain due to randomness.

An environment where actions only require the current percept, with no memory needed.

An environment where current actions depend on the memory of past actions.

An environment that involves only one agent acting on its own.

An environment with multiple agents interacting with each other.

A method to make machines think like humans.

Systems that perceive their environment and take actions.

The setting in which agents operate.

Actions taken by agents that yield desired outcomes.

Agents designed to find solutions to given problems.

The ability to acquire and apply knowledge and skills.

Artificial is made by humans, imitating something natural.

The internal design that enables agents to function.

An environment that does not change while an agent is deciding.

An environment that changes while an agent is deliberating.

An environment with a finite number of percepts and actions.

An environment with an infinite number of actions or percepts.

An environment where the results of all actions are known.

An environment where outcomes are not fully known and require learning.

An environment where complete and accurate information can be obtained.

An environment where information cannot be fully obtained.

AI Agent = Architecture + Agent Program.

The machinery such as sensors and actuators an agent operates on.

An implementation of an agent function that defines actions based on percept sequences.

A mapping from the sequence of percepts to an action: f: P* → A.

Agents that react to current percepts without considering past information.

The categories of agents include simple reflex, model-based reflex, goal-based, and utility-based.

A rule that specifies an action based on a condition being met.

Agents that track the state of the world using current and past information.

Agents that act based on current state and desired goals to determine actions.

Agents that select actions based on achieving the best quality outcome.

Agents that can learn from past experiences to improve future actions.

Part of a learning agent responsible for making improvements from the environment.

A component in a learning agent that provides feedback on performance.

A situation where an agent does not have complete information about the environment.

The solution with the lowest path cost among all options.

A concise problem used to practice or demonstrate techniques.

Problems that occur in reality needing effective solutions.

A simulated problem where an agent vacuums dirt in a grid.

A graphical representation of all possible states in a problem.

Specifies the objects and their locations in the environment.

In a vacuum with n cells, there are n × 2^n states.

The ability of the agent to move within a grid based on obstacles.