Opleiding: AI+ Game Design Agent™

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

• Comprehensive Skill Development
  Master AI-driven game design by integrating procedural generation, adaptive storytelling, and intelligent NPC behavior to create immersive, dynamic gaming experiences.
• Industry Recognition
  Earn a globally recognized certification that highlights your expertise in blending artificial intelligence with creative game development.
• Hands-On Learning
  Practice with real-world projects involving AI-based level design, character behavior modeling, and player experience optimization to sharpen your practical game design skills.
• Career Advancement
  Explore opportunities in AI game development, interactive design, and simulation engineering across gaming studios, tech companies, and entertainment platforms.
• Future-Ready Expertise
  Stay ahead in the next era of gaming innovation with deep knowledge of generative AI, autonomous systems, and adaptive gameplay design.

• 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.