Recommended Prerequisites:

• Basic Programming Skills – Comfortable with Python or similar languages.
• Foundational Math Knowledge – Understanding of linear algebra and probability.
• Intro to Machine Learning – Familiarity with ML concepts and algorithms.
• Game Development Exposure – Experience with Unity or Unreal Engine basics.
• Problem-Solving Mindset – Ability to approach challenges creatively and logically.

Course Outline:

Lesson 1: Introduction to AI in Games

• 1.1 What is AI?
• 1.2 Evolution of AI in the Gaming Industry
• 1.3 Types of AI in Games
• 1.4 Benefits, Challenges, and Innovations in Game AI

Lesson 2: Game Design Principles using AI

• 2.1 Understanding Game Mechanics and Player Experience
• 2.2 Role of AI in Gameplay and Narrative Design
• 2.3 Designing Game Environments for AI Interaction
• 2.4 AI-Driven Behavior vs Traditional Scripted Logic
• 2.5 Case Study: Dynamic AI and Narrative Adaptation in Middle earth: Shadow of Mordor
• 2.6 Hands-On Exercise: Designing Adaptive NPC Behavior and Environment Interaction

Lesson 3: Foundations of AI in Gaming

• 3.1 Core AI Concepts for Gaming
• 3.2 Search Algorithms and Pathfinding
• 3.3 AI Behavior Modeling and Procedural Content Generation (PCG)
• 3.4 Introduction to Machine Learning and Reinforcement Learning
• 3.5 Case Study: AI in Minecraft — Procedural Content Generation and Agent Navigation
• 3.6 Hands-On: Implementing A* Pathfinding and FSM for NPC Behavior

Lesson 4: Reinforcement Learning Fundamentals

• 4.1 Core Concepts: States, Actions, Rewards, Policies, Q-Learning:
• 4.2 Exploration versus Exploitation in Learning Systems:
• 4.3 Overview of Deep Q Networks (DQN) and Policy Gradient Methods
• 4.4 Case Study: Reinforcement Learning in DeepMind’s AlphaGo
• 4.5 Hands-On: Train a Reinforcement Learning Model on OpenAI Gym’s GridWorld

Lesson 5: Planning and Decision Making in Games

• 5.1 Minimax Algorithm and Alpha-Beta Pruning
• 5.2 Monte Carlo Tree Search (MCTS)
• 5.3 Applications in Board Games and Real-Time Strategy (RTS) Games
• 5.4 Case Study: Strategic AI in StarCraft II – Combining Planning Algorithms for Real-Time Strategy
• 5.5 Hands-on Implementation: Guides on implementing the Minimax algorithm for Tic-Tac-Toe

Lesson 6: AI Techniques in 2D/3D Virtual Gaming Environments Basic

• 6.1 Overview of 2D and 3D Game Environments
• 6.2 Environment Representation Techniques
• 6.3 Navigation and Pathfinding in 2D/3D Spaces
• 6.4 Interaction and Behavior Systems in Virtual Environments
• 6.5 Case Study: Navigation and Interaction AI in The Legend of Zelda: Breath of the Wild
• 6.6 Hands-On: Building Basic Navigation and Interaction in 2D and 3D Game Environments

Lesson 7: Adaptive Systems and Dynamic Difficulty

• 7.1 Adaptive Systems Overview
• 7.2 Dynamic Difficulty Adjustment (DDA) Principles
• 7.3 Adaptive Storytelling, Personalization, and Player Profiling
• 7.4 AI Techniques in Adaptive Systems
• 7.5 Implementation Strategies and Tools
• 7.6 Case Study: Dynamic Enemy Management and Replayability with Left 4 Dead’s AI Director
• 7.7 Hands-On: Developing an Adaptive Dynamic Difficulty System in Unity

Lesson 8: Future of AI in Gaming

• 8.1 Generalist AI Agents and Transfer Learning
• 8.2 AI-Powered Game Design and Testing Tools
• 8.3 Ethical Considerations and AI Transparency
• 8.4 Emerging Technologies: VR/AR AI and AI in Esports Coaching

Lesson 9: Capstone Project

All necessary course materials are included.