The AI Architect’s Blueprint: Agents & Task Environments (02.1) скачать в хорошем качестве

The AI Architect’s Blueprint: Agents & Task Environments (02.1)

This lesson introduces the Task Environment, the foundational blueprint every AI agent depends on. You’ll learn how the PEAS framework defines an agent’s world and how seven key environment dimensions determine the true difficulty of any AI problem. From autonomous taxis to simple puzzles, this blueprint shapes how intelligent systems perceive, decide, and act. 🔹 🗺️ Why every AI starts with defining its “world” 🔹 🧰 The PEAS model: performance measures, environments, actuators, sensors 🔹 🚕 How PEAS applies to complex systems like autonomous taxis 🔹 👀 Fully vs. partially observable worlds and why memory matters 🔹 🧑‍🤝‍🧑 Single-agent vs. multi-agent interactions 🔹 🎲 Deterministic vs. stochastic behavior in real environments 🔹 ⏳ Episodic vs. sequential tasks and long-term consequences 🔹 ⏱️ Static, dynamic, and semi-dynamic worlds 🔹 🔢 Discrete vs. continuous state spaces and their complexity 🔹 📚 Known vs. unknown environments and the need for learning 👉 Next video:    • The Evolution of AI Agents: From Reactors ...   The Evolution of AI Agents: From Reactors to Learners (02.2) 👉 Previous video:   • Blueprint for Intelligence: Why Great Soft...   Blueprint for Intelligence: Why Great Software Starts with Great Design (01.3) 📚 Playlist:    • Introduction to Artificial Intelligence 🤖f...   Introduction to Artificial Intelligence for Software Engineers Happy learning with SmartCode Learning.    / @smartcodelearning