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100% fundable

AI+ Game Design Agent™

Intelligent tools and creative design go hand in hand: integrating AI into game design workflows speeds up development, sharpens decision-making, and brings greater depth to digital experiences. This course covers the fundamentals and shows how to put them into practice.

Key topics

  • Building and applying AI-supported design workflows
  • Methods for automated generation of ideas and content
  • Developing adaptive game mechanics using intelligent systems
  • Using data-driven approaches for design decisions
  • Overview of current trend technologies in creative production pipelines

Prerequisite
Familiarity with digital media and an openness to new working methods is all you need to get started.

Target group
Creative professionals in game design, media production, or development who want to advance their work with AI methods will find practical, accessible entry points here.

Technical knowledge meets creative vision – a compact course for anyone who wants to rethink digital game worlds with AI.

course content
  • What are AI agents and how do they work?
  • Building agent architectures and their environments
  • Decision logic and behavioral control at a glance
  • Core principles of multi-agent systems
  • Case study: Pac-Man ghost AI
  • Practical exercise: Building a simple reactive AI agent that moves around in a simple environment using Pygame.
  • Definition and characteristics of AI game agents
  • Key components at a glance
  • Comparing different agent architectures
  • Typical behaviors of AI game agents
  • Case study: Racing games (e.g., Mario Kart, Forza Horizon)
  • Practical exercise: Creating a simple game with moving boxes in Playcanvas.
  • Concepts and core principles of reinforcement learning
  • Algorithms in focus: Q-learning and SARSA
  • Applying RL methods to game agents
  • Common challenges and solutions in game-based RL
  • Case study: AlphaZero in games: Mastering chess, shogi, and Go through self-play and reinforcement learning.
  • Practical exercise: Training a simple RL agent in the OpenAI Gym environment.
  • Viewing NPCs as independent AI agents
  • Basic AI techniques for NPC control
  • Algorithms for calculating optimal paths
  • Avoiding obstacles and optimizing movement
  • Case study
  • Practical exercises
  • Decision trees and minimax algorithm for game AI
  • Monte Carlo tree search (MCTS) for AI agents
  • Utility-based approaches to decision-making
  • AI in real-time strategy (RTS) games
  • Case study: StarCraft II AI by DeepMind
  • Practical exercise: Implementing a simple MCTS agent for Tic-Tac-Toe with Pygame.
  • Characteristics of 3D environments for AI agents
  • Building navigation networks for use in 3D
  • Complex agent behaviors in three-dimensional worlds
  • Case study: The Last of Us
  • Practical exercise: Development of a 3D AI agent with navigation and interaction in Unity using NavMesh and C#.
  • Current developments and emerging AI technologies
  • Perspectives for generalist AI in the gaming industry
  • Case study
  • Task description and objectives
  • Practical implementation of the project
  • Testing, debugging, and optimization
  • Practical exercises for deepening knowledge

Up to 100% funding for your continuing education

Attractive funding opportunities are available for numerous continuing education programs – depending on the funding program, up to 100% funding is possible.
The requirement is that you live or work in Germany. Our funding opportunities are not available outside of Germany.

Funding opportunities are available for career starters, job seekers, career changers, startups, experienced professionals, those seeking career advancement, and specialists.