Module 4 Game Tree Video Explainer скачать в хорошем качестве

Module 4 Game Tree Video Explainer

In this lecture, we explore adversarial search, a fundamental concept in Artificial Intelligence used to model decision-making in competitive environments. Unlike previous modules where an agent operates alone, this module introduces scenarios where multiple agents have conflicting goals and must reason strategically about each other’s actions. You will learn how AI systems use game trees, minimax search, and alpha–beta pruning to make optimal decisions in zero-sum games and adversarial settings. This material forms the foundation for understanding game-playing AI, cybersecurity defense strategies, and multi-agent systems. 📘 Topics Covered in This Lecture: Introduction to adversarial search Game tree representations Minimax algorithm MAX and MIN player strategies Alpha–beta pruning for efficiency Depth-limited search and evaluation functions Why exhaustive search is often impractical 🎯 Learning Objectives: By the end of this lecture, you will be able to: Explain how adversarial search differs from single-agent search Construct and interpret game trees Apply the minimax algorithm to decision-making problems Understand how alpha–beta pruning reduces computation Analyze trade-offs between optimality and efficiency #ArtificialIntelligence #AdversarialSearch #GameTrees #Minimax #AlphaBetaPruning #AIAlgorithms #ComputerScience #CS569 #AIEducation #GameAI