Opleiding: AI+ Game Design Agent Specialty™ eLearning

Empower creators with AI+ Game Design Agent Specialty™ to craft intelligent, dynamic, and immersive gaming experiences.

Become a leader in the next generation of game development by combining creativity with the power of artificial intelligence. This program equips you with the skills to design immersive gaming experiences using procedural content generation, adaptive storytelling, intelligent NPC behavior, and AI-driven gameplay mechanics. Gain hands-on experience through practical projects focused on level generation, character modeling, player behavior analysis, and game experience optimization. Earn a globally recognized certification that validates your expertise in applying AI within modern game development. Discover career opportunities in game studios, simulation engineering, interactive media, and entertainment technology while building future-ready skills in generative AI, autonomous systems, and adaptive game design that prepare you for the rapidly evolving gaming industry.

• 1.1 What are AI Agents?
• 1.2 Agent Architectures and Environments
• 1.3 Decision Making and Behavior Basics
• 1.4 Introduction to Multi-Agent Systems
• 1.5 Case Study: Pac-Man Ghost AI
• 1.6 Hands On: Build a Basic Reactive AI Agent Navigating a Simple Environment Using Pygame

• 2.1 What is an AI Game Agent?
• 2.2 Key Components of AI Game Agent
• 2.3 Agent Architectures
• 2.4 AI Game Agent Behaviors
• 2.5 Case Study: Racing Games (e.g., Mario Kart, Forza Horizon)
• 2.6 Hands-On: Creating a Simple Box Movement Game in Playcanvas

• 3.1 Basics of Reinforcement Learning
• 3.2 Key Algorithms: Q-Learning and SARSA
• 3.3 Applying RL to Game Agents
• 3.4 Challenges and Solutions in Game-based RL
• 3.5 Case Study: AlphaZero in Games: Mastering Chess, Shogi, and Go through Self-Play and Reinforcement Learning
• 3.6 Hands On: Train a simple RL agent in OpenAI Gym environment

• 4.1 Understanding NPCs as AI Agents
• 4.2 Simple AI Techniques for NPCs
• 4.3 Pathfinding Algorithms
• 4.4 Obstacle Avoidance and Movement Optimization
• 4.5 Case Study
• 4.6 Hands-On

• 5.1 Decision Trees and Minimax for Game AI
• 5.2 Monte Carlo Tree Search (MCTS) for AI Agent
• 5.3 Utility-Based Decision Making for Game AI
• 5.4 AI in Real-Time Strategy (RTS) Games
• 5.5 Case Study: StarCraft II AI by DeepMind
• 5.6 Hands-On: Implement a Basic MCTS Agent for Tic-Tac-Toe Using Pygame

• 6.1 3D Environment Representation and Challenges for AI Agents
• 6.2 Navigation Mesh Generation for AI Agents in 3D
• 6.3 Complex Agent Behaviors in 3D Worlds
• 6.4 Case Study: The Last of Us
• 6.5 Hands On: Develop a 3D AI Agent with Navigation and Interaction in Unity Using NavMesh and C#

• 7.1 Current and Future AI Trends
• 7.2 The Future of Generalist AI in Gaming
• 7.3 Case Study

• 8.1. Task Description
• 8.2. Practical Implementation
• 8.3. Testing and Debugging
• 8.4. Hands-on

• Unity ML-Agents
• PyTorch
• TensorFlow
• Python
• OpenAI Gym
• Blender
• Godot Engine
• NVIDIA Omniverse
• Hugging Face Transformers
• Reinforcement Learning Frameworks
• Natural Language Processing Libraries
• Computer Vision SDKs
• Game Analytics Tools
• Behavior Tree Editors
• Procedural Generation Tools
• Speech and Emotion Recognition APIs
• AI Animation Systems
• 3D Simulation Platforms

Online proctored exam included, with one free retake.
Exam format:

50 questions, 70% passing, 90 minutes, online proctored exam

Access to all materials and exams is provided for 365 days after delivery.

Instructor-led OR Self-paced course + Official exam + Digital badge