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Table of Content

Welcome and How to Use This Course

PEAS Representation in Al

Meet PEAS: A Simple Map for Understanding AI Agents

P: Performance Measure

Performance Measures: What Does “Good” Look Like?

E: Environment

Task Environments: Where the Agent Lives and Acts

A: Actuators

Actuators: How Agents Take Action

S: Sensors

Sensors: How Agents Observe the World

Importance of PEAS in Al

Why PEAS Matters: Clear Goals, Better Agents

Exploring Different Types of Al Agents with PEAS Examples

Agent Types Through PEAS: Simple Reflex to Learning Agents

Advantages of PEAS in Al

Strengths of Using PEAS for Designing AI Systems

Disadvantages of PEAS in Al

Limits and Pitfalls: When PEAS Isn’t Enough

Conclusion

Lesson Wrap-Up and Next Steps

Quick Check: Core PEAS Concepts

Quiz: Performance Measures in Real Tasks

Quiz: Understanding Task Environments

Quiz: Matching Sensors and Actuators to Tasks

Final Quiz: Choosing Agent Types with PEAS

One-Minute Survey: How Confident Do You Feel About PEAS?

Unlock a clear, practical framework for designing AI agents with PEAS—Performance measure, Environment, Actuators, and Sensors. PEAS Foundations for AI Agents: Specification, Evaluation, and Comparison is a focused course that helps you think like an AI architect. Instead of jumping straight into models, you’ll learn how to define what “good” looks like, where an agent operates, and how it perceives and acts—so you can build, evaluate, and compare agents with confidence. Through concise lessons and real-world examples, you’ll map diverse tasks—from recommendation systems to robotics—into PEAS specifications that translate directly into better design decisions. You’ll explore performance measures that avoid perverse incentives, classify task environments (stochastic vs. deterministic, episodic vs. sequential, fully vs. partially observable), and match the right sensors and actuators to the job. You’ll also compare agent types—from simple reflex to model-based and learning agents—using PEAS to decide what fits a problem best. By the end, you’ll have a repeatable blueprint for scoping AI projects, communicating requirements, and running fair evaluations. Whether you’re a product manager, data scientist, ML engineer, or student, this course gives you the shared language and tools to move from vague goals to crisp specifications—and from ad hoc solutions to robust, testable AI agents.

PEAS Foundations for AI Agents: Specification, Evaluation, and Comparison | Assignment

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            <p>AI agents make choices, take actions, and learn from results—just like people do in real life. This course gives you a clear, simple way to think about how these agents are built and judged using PEAS: Performance measure, Environment, Actuators, and Sensors. You’ll start with a quick guide on how to use the course, then meet PEAS as a helpful map for understanding any AI agent. From there, you’ll learn what “good” actually means for an agent, where it operates, how it acts, and how it senses the world around it.</p>

<p>Each section focuses on one part of PEAS, with real examples to make ideas stick. You’ll see how different agent types—like simple reflex and learning agents—are chosen based on PEAS, and why setting clear goals leads to better designs. We’ll also talk about the strengths of using PEAS and the moments when it isn’t enough on its own. To check your understanding, short quizzes help you practice matching sensors and actuators to tasks, choosing performance measures, and describing environments. By the end, you’ll be able to specify, evaluate, and compare AI agents with confidence—and know your next steps to keep learning.</p>
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