Intelligent Agents(Algorithms for Intelligent Systems)

  

Unit II: Intelligent Agents

 

Intelligent Agents: Agents and Environments, The Concept of Rationality, The Nature of Environments (PEAS), The Structure of Agents: Simple Reflex Agents, Model-Based Reflex Agents, Goal-Based Agents, Utility-Based agents, Learning Agents.

Agents and Environments:

Agent

An agent is an entity that perceives its environment through sensors and acts upon that environment through actuators. Agents can be anything from simple programs to complex systems like robots or humans. The key characteristic of an agent is its ability to take actions based on its perceptions to achieve specific goals.

Environment

The environment is the external context or world in which the agent operates. It includes everything the agent can perceive and interact with. The environment provides the agent with inputs (percepts) and receives outputs (actions) from the agent.

Example

Consider a self-driving car:

  • Agent: The self-driving car itself, equipped with sensors (cameras, radar, etc.) and actuators (steering, acceleration, brakes).
  • Environment: The road, other vehicles, pedestrians, traffic signals, weather conditions, etc.

In this scenario:

  • The car (agent) perceives the environment through its sensors (e.g., detecting a red traffic light).
  • Based on its programming or learning, it takes actions (e.g., slowing down and stopping).
  • The environment changes in response to the car's actions (e.g., the traffic light turns green, and the car proceeds).

This interaction between the agent and the environment is fundamental to fields like artificial intelligence, robotics, and control systems.

Example: Agent and Environment in the Context of a Robot Vacuum Cleaner

Agent

The agent in this example is the robot vacuum cleaner. It is an autonomous entity designed to perform the task of cleaning floors. The robot vacuum cleaner is equipped with:

·        Sensors: To perceive its environment (e.g., dirt, obstacles, walls).

·        Actuators: To take actions (e.g., moving around, sucking up dirt).

Environment

The environment is the space in which the robot vacuum cleaner operates. This includes:

·        Floor Surfaces: Carpets, tiles, wood, etc.

·        Obstacles: Furniture, walls, toys, etc.

·        Dirt: Dust, crumbs, pet hair, etc.

Example Scenario

·        Initial State: The robot vacuum cleaner starts in the living room. Its sensors detect a patch of dirt near the sofa.

·        Perception: The robot perceives the dirt and also detects the sofa as an obstacle.

·        Decision Making: The robot decides to move towards the dirt while avoiding the sofa.

·        Action: The robot moves to the location of the dirt, avoiding the sofa, and activates its vacuum mechanism to clean the spot.

·        New State: The dirt is removed, and the robot moves on to detect and clean the next area.

This continuous loop of perception, decision making, and action allows the robot vacuum cleaner to effectively clean the environment while navigating around obstacles.

Concept of Rationality

Rationality in the context of agents refers to the ability of an agent to make decisions that maximize its performance measure, given the available information and its knowledge of the environment. A rational agent selects actions that are expected to achieve its goals most effectively, based on what it knows and perceives.

Key Components of Rationality

1.     Performance Measure: A criterion that evaluates how well the agent is doing in terms of achieving its goals.

2.     Prior Knowledge: The information the agent has about the environment before it starts acting.

3.     Percepts: The inputs the agent receives from the environment through its sensors.

4.     Actions: The possible moves or steps the agent can take to influence the environment.

Example: Autonomous Delivery Robot

Consider an Autonomous Delivery Robot designed to deliver packages within an office building.

Performance Measure

·        Goal: Deliver packages to the correct recipients as quickly and efficiently as possible.

·        Performance Criteria: Minimize delivery time, avoid collisions, ensure packages are delivered to the right recipients.

Prior Knowledge

·        The robot knows the layout of the office building, including the locations of rooms, doors, and elevators.

·        It has a list of packages to deliver, including the recipient's name and location.

Percepts

·        The robot uses sensors to perceive its environment, such as detecting obstacles (e.g., people, furniture), recognizing room numbers, and identifying recipients.

Actions

·        The robot can move forward, backward, turn, stop, and use elevators.

·        It can also interact with recipients to hand over packages.

Rational Decision-Making Process

1.     Perception: The robot perceives its current location and detects an obstacle (e.g., a closed door) in its path.

2.     Decision Making: Based on its prior knowledge, the robot knows there is an alternative route through another corridor. It calculates that taking this route will still allow it to deliver the package on time.

3.     Action: The robot decides to take the alternative route, avoiding the closed door.

4.     Feedback: The robot successfully navigates the alternative route, delivers the package to the correct recipient, and receives a confirmation that the package has been delivered.

Rationality in Action

·        Optimal Decision: The robot's decision to take the alternative route is rational because it maximizes the performance measure by ensuring timely delivery while avoiding collisions.

·        Adaptability: If the robot encounters an unexpected obstacle (e.g., a new piece of furniture), it reassesses the situation and makes a new rational decision, such as finding another path or waiting for the obstacle to move.

The Nature of Environments (PEAS)

PEAS stands for Performance measure, Environment, Actuators, and Sensors. It is a framework used to define the task environment of an intelligent agent. By specifying these components, we can clearly understand the nature of the environment in which the agent operates and how it should perform its tasks.

Components of PEAS

1.     Performance Measure:

o   Definition: Criteria that evaluate how well the agent is performing its task.

o   Example: For a self-driving car, the performance measure could include safety, fuel efficiency, and passenger comfort.

2.     Environment:

o   Definition: The external context or world in which the agent operates.

o   Example: For a self-driving car, the environment includes roads, traffic, pedestrians, weather conditions, and traffic laws.

3.     Actuators:

o   Definition: The mechanisms through which the agent acts upon the environment.

o   Example: For a self-driving car, actuators include the steering wheel, accelerator, brake, and turn signals.

4.     Sensors:

o   Definition: The devices through which the agent perceives the environment.

o   Example: For a self-driving car, sensors include cameras, radar, LIDAR, and GPS.

Examples of PEAS for Different Agents

1. Autonomous Vacuum Cleaner

·        Performance Measure: Cleanliness of the floor, battery life, time taken to clean.

·        Environment: Rooms, furniture, dirt, and obstacles.

·        Actuators: Wheels, brushes, vacuum mechanism.

·        Sensors: Dirt detection sensors, obstacle detection sensors, cliff sensors.

2. Medical Diagnosis System

·        Performance Measure: Accuracy of diagnosis, speed of diagnosis, patient outcomes.

·        Environment: Patient data, medical history, symptoms, test results.

·        Actuators: Display recommendations; generate reports, alert medical staff.

·        Sensors: Input devices for patient data, interfaces for test results.

3. Online Shopping Recommender System

·        Performance Measure: Customer satisfaction, sales conversion rate, relevance of recommendations.

·        Environment: User profiles, browsing history, product database, current trends.

·        Actuators: Display recommendations, send notifications, update user profiles.

·        Sensors: Clickstream data, purchase history, user feedback.

Detailed Example: Self-Driving Car

Performance Measure

·        Safety: Minimize accidents and ensure passenger safety.

·        Efficiency: Optimize fuel consumption and travel time.

·        Comfort: Provide a smooth and comfortable ride.

Environment

·        Roads: Highways, city streets, rural roads.

·        Traffic: Other vehicles, pedestrians, cyclists.

·        Conditions: Weather (rain, snow, fog), road conditions (potholes, construction).

·        Regulations: Traffic laws, speed limits, traffic signals.

Actuators

·        Steering Wheel: Control the direction of the car.

·        Accelerator: Control the speed of the car.

·        Brake: Slow down or stop the car.

·        Turn Signals: Indicate turning or lane changing.

Sensors

·        Cameras: Capture visual information about the surroundings.

·        Radar: Detect the distance and speed of nearby objects.

·        LIDAR: Create a 3D map of the environment.

·        GPS: Determine the car's location and navigate routes.

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