Understanding PEAS in Artificial Intelligence

We know that there are several sorts of AI agents. The PEAS System is used to group related agents together. The PEAS system provides the performance measure for the respective agent’s environment, actuators, and sensors. The majority of the top-performing agents are Rational Agents.

Rational Agent: The rational agent evaluates all options and selects the most efficient action. For example, it selects the shortest road with the lowest cost for maximum efficiency. PEAS is a term that stands for Performance Measure, Environment, Actuator, and Sensor.

1. Performance Measure: Performance measure is the unit used to define an agent’s success. The performance of agents changes according to their distinct principles.
2. Environment: The environment is an agent’s immediate surroundings. If the agent is set in motion, it changes over time. There are five primary types of environments:
   1. Fully Observable & Partially Observable
   2. Episodic & Sequential
   3. Static & Dynamic
   4. Discrete & Continuous
   5. Deterministic & Stochastic
3. Actuator: An actuator is a component of the agent that provides the action’s output to the environment.
4. Sensor: Sensors are the receptive components of an agent that receive input.

PEAS for self-driving cars

Let’s suppose a self-driving car then PEAS representation will be:

• Performance: Safety, time, legal drive, comfort
• Environment: Roads, other vehicles, road signs, pedestrian
• Actuators: Steering, accelerator, brake, signal, horn
• Sensors: Camera, GPS, speedometer, odometer, accelerometer, sonar.

PEAS for Vacuum Cleaners

Let’s suppose a vacuum cleaner then PEAS representation will be:

• Performance: cleanness, efficiency, battery life, security.
• Environment: room, table, wood floor, carpet, different obstacles
• Actuators: wheels, different brushes, vacuum extractors.
• Sensors: camera, dirt detection sensor, cliff sensor, bump sensors, infrared wall sensors.

Example of Agents with their PEAS representation