BPSY361: Introduction to AI Quiz

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Questions and Answers

What is an Agent, according to the text?

An entity that can independently make decisions and act upon its environment.

A system that can perceive its environment and act upon it through sensors and effectors. (correct)

A computer program that can perform tasks automatically.

A human being capable of reasoning and making choices.

Which of the following is NOT a component of the agent function?

Percept Sequence

Actions

Agent's Goals (correct)

Performance Measure

What does the term 'Percept Sequence' refer to?

The complete history of all the agent's sensory inputs. (correct)

The sequence of actions an agent takes in response to its environment.

The agent's internal representation of its environment.

The agent's current perception of its environment.

What is rationality in the context of agents?

The ability to make decisions that are logically sound and lead to the best possible outcome. (D) Signup and view all the answers

What are the four factors that determine rationality of an agent?

Performance Measure, Agent's Knowledge, Actions, and Agent's Percept Sequence. (D) Signup and view all the answers

Which of the following can be considered a sensor for a robotic agent?

A camera. (B) Signup and view all the answers

Which of the following is NOT an example of an effector?

A computer screen. (C) Signup and view all the answers

What is a defining characteristic of a Sequential task environment?

Decisions made in the past can impact future choices. (D) Signup and view all the answers

Which scenario exemplifies a Discrete task environment?

A chess game where each player has a finite number of moves. (D) Signup and view all the answers

What makes a Dynamic task environment distinct from a Static environment?

Dynamic environments continuously evolve while static environments remain unchanged. (A) Signup and view all the answers

In an Episodic task environment, how does the agent's current action relate to previous actions?

The current action is completely independent of previous actions. (D) Signup and view all the answers

Which of the following is NOT a property of task environments discussed in the provided content?

Deterministic vs. Stochastic (B) Signup and view all the answers

Which example best illustrates a Static task environment?

An empty room with no activity taking place. (A) Signup and view all the answers

How would you describe the environment of a robot that picks up and sorts objects on a conveyor belt?

Episodic, Discrete, and Dynamic (D) Signup and view all the answers

What is the primary factor that differentiates a Dynamic task environment from a Static environment?

The changing state of the environment over time. (A) Signup and view all the answers

Which of the following statements accurately describes an Episodic task environment?

Each action the agent takes is independent of previous actions. (A) Signup and view all the answers

Which of the following demonstrates a Dynamic task environment?

A driver navigating a highway with changing traffic conditions. (D) Signup and view all the answers

Which component of a problem denotes the agent's starting position?

Initial State (C) Signup and view all the answers

What does the Transition Model describe in a problem-solving context?

It specifies the result of each action from a given state. (B) Signup and view all the answers

What is the purpose of the Goal Test in the problem-solving process?

To evaluate if the specified goal has been achieved. (A) Signup and view all the answers

How do the initial state, actions, and transition model interact?

They define the state space of a problem. (B) Signup and view all the answers

In the context of problem-solving agents, what does the term 'Actions' refer to?

The actions that can be executed in the current state. (B) Signup and view all the answers

According to the definition of a rational agent, what is the most important factor in choosing an action?

The agent's expected performance based on its actions. (C) Signup and view all the answers

Which of the following is NOT a characteristic of a rational agent, as defined in the content?

The ability to predict the future with absolute certainty. (B) Signup and view all the answers

What is the purpose of the performance measure in the vacuum cleaner example?

To evaluate the agent's effectiveness in cleaning the environment. (A) Signup and view all the answers

In the context of the vacuum cleaner example, why is the agent's initial location considered unknown?

The agent must learn its initial location through its actions. (D) Signup and view all the answers

Which of these concepts is BEST defined by the notion that a rational agent should adapt its actions based on new information gathered from its experiences?

Learning (C) Signup and view all the answers

What does the concept of autonomy suggest about a rational agent?

It should be able to function even with incomplete or inaccurate information. (B) Signup and view all the answers

Which of these is NOT considered a key characteristic of a rational agent as described in the content?

Taking only pre-programmed actions (D) Signup and view all the answers

What is the main difference between a rational agent that is 'omniscient' and one that is 'learning'?

An omniscient agent has complete knowledge of the environment, while a learning agent must acquire knowledge through experience. (D) Signup and view all the answers

Which statement about a rational agent's performance measure is MOST accurate?

It can be designed to reward different types of actions and outcomes. (B) Signup and view all the answers

What kind of environment is characterized by actions that cannot be easily numbered or categorized?

Continuous (D) Signup and view all the answers

In a known environment, what is readily available to the agent?

The output for all possible actions (B) Signup and view all the answers

What is the defining difference between a simple reflex agent and a model-based reflex agent?

The capacity to store and use a model of the environment (A) Signup and view all the answers

What is the role of an agent program in the structure of an agent?

It implements the agent function, mapping percepts to actions. (B) Signup and view all the answers

Which of these is NOT a type of agent mentioned in the provided content?

Reactive agent (A) Signup and view all the answers

What characterizes a simple reflex agent's decision-making process?

It bases decisions solely on the current percept. (C) Signup and view all the answers

What is the function of condition-action rules in a simple reflex agent?

To connect percepts to actions. (A) Signup and view all the answers

Which of the following is an example of a simple reflex agent?

A thermostat that adjusts the temperature based on the current room temperature. (B) Signup and view all the answers

Which of these is a key characteristic of a goal-based agent?

It pursues a specific desired state or outcome. (B) Signup and view all the answers

What do you understand by the term 'architecture' in the context of agent structures?

The physical hardware or platform that the agent runs on. (C) Signup and view all the answers

Study Notes

Course Information

Course Title: BPSY361: Artificial Intelligence (AI)
Institution: CHRIST (Deemed to be University)
Location: Bangalore, India
Mission: Holistic development to contribute effectively
Vision: Excellence and Service
Core Values: Faith in God, Moral Uprightness, Love of Fellow Beings

Unit 1: Introduction

Introduction to AI: Covers basic concepts, intelligent agents, agents and environments, good behavior, nature of the environments, structure of agents, problem-solving, and examples of problems

Artificial Intelligence (AI)

Artificial: Created by humans, in imitation of natural things

What is Artificial Intelligence in AI?

AI uses computers and machines to mimic human problem-solving and decision-making abilities.

What is Intelligence?

Ability to acquire and apply knowledge and skills
AI is a method to make a computer, computer-controlled robot, or software think intelligently like a human mind
Psychologists define intelligence as the ability to learn, recognize and solve problems

Intelligence (Brain and its Hemispheres)

Stronger connection between brain hemispheres
Einstein (1905): Had more extensive connections between certain parts of his cerebral hemispheres compared to younger and older control group brains.

Introduction to AI (Field, Science, and Data)

AI is a field that combines computer science and robust datasets to enable problem-solving

A Brief History of Artificial Intelligence

1956: John McCarthy coined the term "artificial intelligence" and held the first AI conference.
1969: Shakey, the first general-purpose mobile robot.
1997: IBM's Deep Blue defeated a world chess champion.
2002: First commercially successful robotic vacuum cleaner.
2005-2019: Advancements in speech recognition, robotic process automation (RPA), and smart homes.
2020: Baidu's LinearFold AI algorithm developed a Covid-19 vaccine faster than previously possible

Intelligent Agents

Agent: A computer program or system designed to perceive its environment.
Make decisions and take actions to achieve specific goals.
Operate autonomously.
Diagram shows sensors, percepts, environment, actions, and effectors.

Intelligent Agents: Agents and Environments

Rational Agents: Logic or Reason based systems called intelligent
Agent: Anything capable of perceiving the environment with sensors and acting upon it with actuators.
Agent behavior is defined by an agent function that maps percept sequences to actions.
Diagram shows Agents interacting with environment through sensors and actuators

Agents and Environments (Agent Function)

Agent function: Abstract mathematical description of an agent, contrasting with the concrete implementation (agent program), which runs within a physical system

Example: Vacuum-Cleaner World

Diagram shows a vacuum-cleaner world with two locations (A and B).
Table displays percept sequences and corresponding actions.

What is Agent and Environment?

An agent is something that senses and acts upon its environment.
Humans have sensory organs (eyes, ears, etc.) and effectors.
Robotic agents use sensors like cameras, IR, and effectors like motors.
Software agents have coded programs and actions.

Agent Terminology

Performance Measure: Criteria for evaluating an agent's success.
Behavior: Action taken by the agent based on percepts.
Percept: Input received by the agent at a specific time.
Percept Sequence: The history of all external inputs the agent has received up to a given point.
Agent Function: Mapping from percept sequence to action.

Good Behavior: The Concept of Rationality

A rational agent does the right thing conceptually, ensuring correctness of every entry in its agent function table.
The agent's rationality depends on the performance measure, prior environment knowledge, actions, and current percept sequence.

Definition of a Rational Agent

For each possible percept sequence, a rational agent selects the action it expects to maximize its performance measure given the evidence provided by the percept sequence and built-in knowledge.

Rules for AI agents

Rule 1: An AI agent must have the ability to perceive the environment.
Rule 2: Observation must be used for decision-making.
Rule 3: Decisions must result in an action.
Rule 4: Actions must obey a rational course of action

Example (Performance Measure)

The performance measure awards one point for each clean square at each time step during 1,000 steps.
Environment geography is known but dirt distribution and initial agent location are not.
Actions are Left, Right, and Suck

Omniscience, Learning, and Autonomy

Omniscience: An agent knows the outcome of its actions, learns through information gathering, and adapts with pre-existing knowledge.
Autonomy: Agent learns from its environment to compensate for partial or incomplete knowledge

The Nature of Environments

Task Environments are essentially problems where solutions are rational agents.
PEAS is a model covering Performance, Environment, Actuators, and Sensors

Properties of Task Environments

Fully Observable vs. Partially Observable (environment and agent)
Deterministic vs. Stochastic (environment's state)
Episodic vs. Sequential (temporal dependency between actions)
Dynamic vs. Static (if environment continuously changes)
Discrete vs. Continuous (number of possible actions or steps)
Known vs. Unknown (agent’s knowledge of environment)

Structure of Agents

Agent = Architecture + Agent Program
Architecture is machinery. Sensors and Actuators.
Agent Program is an implementation of an agent function.
Agent Function is a map from percept sequence (current agent's perception) to action.

Structure of Agents (Agent Types)

Simple reflex agents
Model-based reflex agents
Goal-based agents
Utility-based agents
Learning Agent

Simple Reflex Agents

Simplest type. Directly responds to percepts.
Uses condition-action rules (if condition then action).
Example: Car braking given in-front car braking.

Model-based Reflex Agents (World Tracking)

Handles partial observability by tracking the world.
Combines current percept and previous internal state to update world knowledge.

Goal-Based Agents (Specific Desirable States)

Agents have descriptions of desirable states (goals).
Action selection depends on goal states.

Utility-Based Agents (Quality of Action)

Agents choose actions based on utility (quality) rather than simply reaching a goal.
Selects actions that lead to more reliable and more desirable outcomes

Learning Agent (Adaptable Agent)

Learns from past experiences and adapts automatically.
Component parts include: Learning Element, Critic, Performance Element, and Problem Generator

Problem Solving Agents

Problem-solving agents determine sequences of actions toward a desirable state.
Search is a method to find such a sequence, involving a systematic exploration of alternative actions.
Problem definition is a detailed specification of acceptable solutions and inputs.
Problem analysis, Knowledge Representation, and problem solving techniques are parts of the process.

Components of Problem-solving (Phase descriptions)

Goal Formulation: Agent formulates specific steps to reach target/goal.
Problem Formulation: Fundamental step in the problem-solving process, it details what action to take to reach the goal.
Search: Finding the optimal sequence of actions that will achieve the goal. Simulates multiple potentially unsuccessful sequences of actions.
Execution: Executing a sequence of actions that was identified through search

Components of a problem (Formal definitions for the process)

Initial State: Agent's starting state or initial step toward the goal.
Actions: Possible actions an agent can take (based on the given state).
Transition Model: Describes the result of an action in a given state.
Goal Test: Checks if the state matches the goal.
Path Cost: An agent's numerical cost of leading to the goal (usually the goal's lowest possible cost to determine optimal solutions).

Example Problems (Problem types)

Standardized/Toy Problems: Used to demonstrate problem-solving techniques (e.g., puzzles).
Real-world problems: Require solutions for scenarios in the real world (e.g., route-finding, touring problems, Traveling Salesman Problem).

Example problems (Specific Problem types)

8-puzzle: A puzzle where tiles need to be arranged in a specific order.
8-queens problem: A chess puzzle where 8 queens need to be placed on a board so that no two queens attack each other.
Route-finding problem: To find the shortest route between locations.
Touring problems: To visit all locations or cities once and return to the starting location

The Traveling Salesman problem

The salesman must travel to all cities once before returning home.
Objective: Minimize the total distance traveled

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Description

This quiz tests your understanding of the basic concepts of Artificial Intelligence as covered in Unit 1 of BPSY361. It focuses on intelligent agents, their environments, problem-solving methods, and the definition of intelligence. Prepare to explore how AI mimics human decision-making abilities!