Opleiding: AI+ Game Design Agent Specialty™

Formerly known as AI+ Game Design Agent™

Empower creators with AI+ Game Design Agent Specialty™ 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.

Aspiring Game Designers – Perfect for those who want to integrate AI into storytelling, mechanics, and player experiences.

AI Enthusiasts – Ideal for learners eager to explore how AI can enhance creativity and interactivity in games.

Game Developers – Great for professionals aiming to build intelligent systems, adaptive gameplay, and smart NPCs.

Digital Artists – Excellent for creatives interested in using AI to design immersive environments and dynamic game elements.

Tech Entrepreneurs – Ideal for innovators looking to leverage AI in building the next generation of interactive gaming platforms.

• Next-Gen Game CreationLearn to design intelligent, adaptive games that respond dynamically to player behavior and choices.
• Industry-Relevant ExpertiseGain skills at the intersection of AI, creativity, and game design—highly sought after in modern studios.
• Hands-On InnovationBuild real-world projects integrating AI-driven storytelling, procedural worlds, and smart NPC systems.
• Career AccelerationStand out for roles in AI game development, systems design, and creative technology leadership.
• Future-Ready SkillsPrepare for the evolving gaming landscape where AI shapes creativity, engagement, and interactive storytelling.

• 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.

De OCICT maatwerk-methode houdt in dat op basis van de behoefte van de klant - en indien nodig na een intakegesprek - een individueel opleidingstraject wordt opgesteld. De trainingen worden veelal individueel gevolgd, zodat iedere training een maatwerktraject is. Klassikale trainingen zijn natuurlijk mogelijk, mits ze aansluiten bij de leerbehoeften van de deelnemers / opdrachtgever.

Alle onderdelen van de training kennen een voortdurende wisselwerking tussen theorie en praktijk. De inhoud van de training kan vooraf per cursist worden aangepast en tijdens de cursus worden bijgesteld. De focus ligt op maatwerk: het traject wordt afgestemd op de doelstellingen van de deelnemer(s) en/of opdrachtgever, ongeacht of het individueel of klassikaal wordt aangeboden.