In the previous topic, we learned that an AI Agent is something that perceives its environment through sensors and acts upon it using actuators to achieve goals intelligently.
Now comes the next question…
👉 Where does this agent live and work?
👉 How do we describe the world around it?
That’s exactly what the concept of Task Environment and PEAS framework helps us understand.
What Is a Task Environment?
Every agent works inside some environment — a world full of conditions, people, or situations that affect how it behaves.
For example:
- A vacuum cleaner agent works in your living room.
- A self-driving car agent works on roads with traffic and pedestrians.
- A medical AI agent works inside a hospital system.
To design or study any intelligent agent, we must clearly describe PEAS Framework:
The PEAS Framework — Defining an Agent’s World
PEAS stands for:
P – Performance Measure,
E – Environment,
A – Actuators, and
S – Sensors.
| Letter | Full Form | Meaning |
| P | Performance Measure | How we judge the agent’s success (its goal). |
| E | Environment | The surroundings where the agent operates. |
| A | Actuators | The tools or parts the agent uses to take action. |
| S | Sensors | The parts that let the agent sense or perceive the world. |
🧩 Think of PEAS like the “job description” of an AI agent.
Before we build it, we must define what it needs to do, where it will work, and what tools it will use.
Example: PEAS for a Self-Driving Taxi Agent
| Element | Description |
| Performance Measure (P) | Safe, fast, legal, comfortable trips, maximize profits |
| Environment (E) | Roads, traffic, pedestrians, customers, weather |
| Actuators (A) | Steering, accelerator, brake, horn, display, lights |
| Sensors (S) | Cameras, GPS, sonar, speedometer, engine sensors, keyboard |
How It Works:
- The sensors collect information — like road conditions, nearby cars, or traffic lights.
- The agent’s brain (AI program) decides what action to take — accelerate, stop, or turn.
- The actuators perform that action — by controlling the steering or brakes.
- The agent’s performance is measured — was the trip safe, fast, and comfortable?
So, the PEAS model gives a complete picture of how an agent interacts with its world.
Other PEAS Examples
| Agent Type | Performance Measure | Environment | Actuators | Sensors |
| Medical Diagnosis System | Healthy patient, reduced cost | Patient, hospital, staff | Display of questions, tests, treatments | Touchscreen or voice input |
| Satellite Image Analyzer | Correct categorization of terrain | Orbit, weather, communication link | Display classification results | High-resolution digital camera |
| Part-Picking Robot | Percentage of correctly sorted parts | Conveyor belt with bins | Robotic arm, gripper | Camera, tactile sensors |
| Refinery Controller | Purity, yield, safety | Refinery and raw materials | Valves, pumps, heaters | Pressure, flow, temperature sensors |
| Interactive English Tutor | Student’s test score | Classroom, students | Display exercises, give feedback | Keyboard, microphone |
Each of these examples shows that every agent works in a different type of world, has different tools, and is judged by different goals.
In real life, a doctor, a driver, and a teacher all have different goals, tools, and environments.
Similarly, AI agents need a proper setup to work efficiently — and PEAS helps us define that setup.
Summary
✅ Every AI agent works in a specific task environment.
✅ The PEAS model defines this environment in four parts — Performance, Environment, Actuators, and Sensors.
✅ Each agent has its own unique PEAS setup depending on what problem it solves.
✅ Understanding PEAS helps us design more effective and goal-oriented AI systems.
Properties of Task Environment in Artificial Intelligence
In the previous topic, we learned about PEAS (Performance Measure, Environment, Actuators, and Sensors) — a framework that helps us describe what an agent needs to do and where it works.
But now comes the next question:
👉 What kind of environment does the agent live in?
To answer these questions, we study the Properties of Task Environments — they help us understand the nature and complexity of the world around an AI agent.
What are the Properties of Task Environments?
Every agent operates in a specific environment.
These environments can vary in seven important ways, which affect how the agent should be designed and how intelligent it needs to be.
Let’s explore each property with simple explanations and examples 👇
🧩 1. Fully Observable vs. Partially Observable
- Fully observable = the agent can “see everything” it needs to make a perfect decision.
- Partially observable = it can see only part of the picture.
🧠 Examples:
- Fully observable: A chess AI — it can see the whole board.
- Partially observable: A robot vacuum — it only knows about the dirt directly under it.
🤖 2. Single Agent vs. Multi-Agent
Single agent = works alone.
Multi-agent = many agents interact (either help or compete).
🧠 Examples:
Single: Robot vacuum cleaning the floor.
Multi:
Competitive → Chess (two players).
Cooperative → Self-driving cars on the road (avoiding crashes together).
⚙️ 3. Deterministic vs. Stochastic
- Deterministic = outcome is fixed, predictable.
- Stochastic = outcome can change randomly.
🧠 Examples:
- Deterministic: Robot arm picking parts in a factory — same result every time.
- Stochastic: Self-driving car — anything can happen (a dog crosses, traffic lights fail).
⏳ 4. Episodic vs. Sequential
Episodic = each action is separate.
Sequential = each action affects what happens next.
🧠 Examples:
Episodic: Email spam filter (each email is separate).
Sequential: Taxi driving — one wrong turn changes your next move!
5️⃣ Static vs. Dynamic
Static environment → The world waits while the agent thinks.
Nothing changes until the agent takes an action.
Dynamic environment → The world keeps moving even while the agent is thinking!
| Type | What Happens | Example |
| Static | The situation stays still until you decide what to do. | When you play chess, the pieces don’t move until you make your move. You can think for 10 minutes, and the board stays the same. |
| Dynamic | Things around you keep changing even while you are deciding. | When you’re driving a car, traffic lights change, people walk, and cars move — even if you pause to think, the world keeps moving! |
🧮 6. Discrete vs. Continuous
- Discrete = has steps or turns.
- Continuous = changes smoothly all the time.
🧠 Examples:
- Discrete: Chess (turn by turn).
- Continuous: Self-driving car (speed and steering always changing).
💭 7. Known vs. Unknown
Known = agent knows the rules.
Unknown = agent has to learn the rules.
🧠 Examples:
Known: Factory robot assembling parts with fixed instructions.
Unknown: Playing a new video game for the first time — you learn as you go.
Summary Table – Properties of Task Environment
| Property | Type 1 | Type 2 | Simple Example |
| Observability | Fully Observable | Partially Observable | Chess vs. Vacuum robot |
| Agents | Single | Multi | Robot cleaner vs. Self-driving cars |
| Determinism | Deterministic | Stochastic | Factory robot vs. Weather forecast |
| Episodes | Episodic | Sequential | Spam filter vs. Taxi driver |
| Change | Static | Dynamic | Sudoku vs. Car driving |
| State | Discrete | Continuous | Board game vs. Real-world motion |
| Knowledge | Known | Unknown | Factory robot vs. Exploration robot |