AI Agents and Accessibility Quiz

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Questions and Answers

What defines an accessible environment?

  • An environment lacking any measurable parameters.
  • An environment with unpredictable outcomes.
  • An environment defined solely by its physical dimensions.
  • An environment where the agent can obtain complete and accurate information. (correct)

Which of the following represents an inaccessible environment?

  • A simple maze that an agent is navigating.
  • An experiment conducted in a controlled laboratory.
  • A room with known temperature.
  • An event happening in another part of the world. (correct)

Which two components make up an AI agent?

  • Architecture and Agent Program (correct)
  • Sensors and Decision-Making
  • Input and Output Mechanisms
  • Data and Processing Power

What is a simple reflex agent primarily based on?

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

What does the agent function map from and to?

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

Which type of agent incorporates knowledge of previous actions?

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

What mechanism do utility-based agents use to make decisions?

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

Which of the following is NOT a type of AI agent mentioned?

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

What characterizes a single-agent environment?

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

Which type of environment requires an agent to continuously observe its surroundings?

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

What defines a discrete environment?

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

In which scenario does an agent operate in a known environment?

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

How does a multi-agent environment differ from a single-agent environment?

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

Which of the following is an example of a continuous environment?

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

Which of the following statements accurately reflects a static environment?

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

What distinguishes an unknown environment for an agent?

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

What is the primary function of a model-based reflex agent?

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

What differentiates goal-based agents from other types of agents?

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

Which factor is crucial for utility-based agents when selecting an action sequence?

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

What role does the 'critic' play in a learning agent?

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

What is a characteristic feature of a learning agent?

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

How does a model-based reflex agent deal with partial observability?

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

Which statement best describes the actions of a goal-based agent?

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

What is the primary objective of utility-based agents in action selection?

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

What defines the optimal solution in problem solving?

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

What is the primary purpose of standardized/toy problems?

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

How many states are there in a simple two-cell vacuum world?

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

What characteristic distinguishes real-world problems from standardized/toy problems?

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

In the context of the vacuum world problem, what can obstruct an agent's movement?

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

What is the structure used to represent the vacuum world in the provided example?

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

What does the agent in the vacuum world do?

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

What does the state space graph represent in the vacuum world?

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

What is the primary goal of Artificial Intelligence (AI)?

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

Which of the following is NOT a core value of CHRIST Deemed to be University?

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

What is the key characteristic of an "artificial" element within the context of AI?

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

What is the primary purpose of an Intelligent Agent in AI?

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

What is the difference between "intelligence" and "artificial intelligence"?

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

What is the primary difference between a problem-solving agent and a traditional computer program?

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

According to the content, what is the main focus of the "Introduction to AI" unit?

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

Based on the text, what is the primary characteristic of "Good behavior" in an Intelligent Agent?

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

What type of environment is characterized by an agent's inability to completely determine the next state based solely on its current state and chosen action?

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

Which environment type requires an agent to maintain a memory of past actions to make informed decisions?

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

In what kind of environment is an agent's sensor capable of perceiving the complete state of the world at any given time?

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

Which of the following is NOT a characteristic of an environment from the perspective of an agent, as per Russell and Norvig?

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

When an environment is considered 'unknown,' what does that mean for the agent?

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

What type of environment is characterized by a series of independent, one-shot actions where the agent only needs the current information to make a decision?

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

An environment where an agent's actions have a predictable outcome, allowing for complete control over the next state, is considered:

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

Which of the following is a key characteristic of an environment that is considered 'accessible'?

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

Flashcards

Fully Observable Environment

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

Partially Observable Environment

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

Deterministic Environment

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

Stochastic Environment

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

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Episodic Environment

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

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Sequential Environment

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

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Single-Agent Environment

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

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Multi-Agent Environment

An environment with multiple agents interacting with each other.

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Artificial Intelligence

A method to make machines think like humans.

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Intelligent Agents

Systems that perceive their environment and take actions.

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Nature of Environments

The setting in which agents operate.

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Good Behavior in AI

Actions taken by agents that yield desired outcomes.

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Problem-Solving Agents

Agents designed to find solutions to given problems.

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Definition of Intelligence

The ability to acquire and apply knowledge and skills.

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Artificial vs. Natural

Artificial is made by humans, imitating something natural.

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Structure of Agents

The internal design that enables agents to function.

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Static Environment

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

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Dynamic Environment

An environment that changes while an agent is deliberating.

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Discrete Environment

An environment with a finite number of percepts and actions.

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Continuous Environment

An environment with an infinite number of actions or percepts.

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Known Environment

An environment where the results of all actions are known.

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Unknown Environment

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

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Accessible Environment

An environment where complete and accurate information can be obtained.

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Inaccessible Environment

An environment where information cannot be fully obtained.

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Structure of an AI Agent

AI Agent = Architecture + Agent Program.

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Architecture of an Agent

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

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Agent Program

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

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Agent Function

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

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Simple Reflex Agents

Agents that react to current percepts without considering past information.

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Types of Agents

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

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Condition Action Rule

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

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Model-based Reflex Agents

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

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Goal-based Agents

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

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Utility-based Agents

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

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Learning Agent

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

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Learning Element

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

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Critic

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

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Partial Observability

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

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Optimal Solution

The solution with the lowest path cost among all options.

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Standardized Problem

A concise problem used to practice or demonstrate techniques.

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Real-world Problems

Problems that occur in reality needing effective solutions.

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Vacuum World Problem

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

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State Space Graph

A graphical representation of all possible states in a problem.

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World State

Specifies the objects and their locations in the environment.

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Number of States

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

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Agent Movement

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

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Study Notes

Artificial Intelligence (AI) Overview

  • AI is a method of making computers, robots, or software think like humans.
  • This involves mimicking human problem-solving and decision-making abilities.
  • AI leverages computers and machines to achieve this result.

Core Concepts

  • Intelligence: The ability to acquire and apply knowledge and skills. Psychologists see it as learning, problem-solving, and recognizing problems.

  • Agent: Anything that perceives the environment via sensors and acts upon it through actuators. These can be people, robots, or computer programs.

    • Structure of Agents: Combining architecture (physical/software elements) with a program (instructions).
  • Environment: Everything that surrounds the agent, excluding the agent itself.

    • Nature of Environments:
      • Fully observable vs Partially observable: How much information is directly available to the agent.
      • Static vs Dynamic: Does the environment change while the agent is making decisions.
      • Discrete vs Continuous: Is it possible to take an infinite number of steps or actions; are there a finite number.
      • Deterministic vs Stochastic: Can the future of the environment's state be determined from the agent's current state and action.
      • Single-agent vs Multi-agent: One agent acting, or multiple agents interacting in the same environment.
      • Episodic vs Sequential: Does the agent need to store information about past actions/states.
      • Known vs Unknown: Does the agent know the rules/mechanisms of the environment from the outset.
      • Accessible vs Inaccessible: Is full access to the environment's state permitted.
  • Sensors: Devices that detect changes in the environment and send information.

  • Actuators: Mechanisms that convert energy into motion—responsible for performing actions.

  • Effectors: The devices that affect the environment (e.g., legs, wheels, arms).

  • PEAS Representation: A model for describing properties of an AI agent.

    • P: Performance Measure (e.g., time efficiency, accuracy)
    • E: Environment
    • A: Actuators
    • S: Sensors

Learning Agents

  • Agents that can learn from past experiences.
  • They start with basic knowledge and adapt.
  • Key components:
    • Learning Element: Improves based on experience.
    • Critic: Provides feedback on agent performance.
    • Performance Element: Selects actions in the environment.
    • Problem Generator: Suggests useful actions to improve learning.

Problem Solving Agents

  • Agents that decide actions by finding sequences that lead to a desired state or solution.

  • Use search in their computation to decide.

  • Problem Formulation Components needed:

    • Initial State: Agent's starting point.
    • Actions: Possible agent actions.
    • Transition Model: Results of each action in the environment.
    • Goal Test: Identifies if the current state is the goal state.
    • Path Cost: Numerical cost of each path to goal.
  • Types of Problems:

    • Standardized/Toy Problems: Designed for demonstration/testing, simply described.
    • Real-world Problems: More complex tasks with the need of thorough solutions.

Example Problems

  • Vacuum World Problem: Agents move on a grid to suck up dirt.
  • Grid World Problem: Agents navigate a matrix of cells that may contain obstacles.
  • Eight Puzzle Problem: Tiles must be rearranged to meet a goal state (order).

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