AI+ Gaming™

Price
Net: 397,00 €
VAT.: 75,43 €

Duration
1 day

For companies and job seekers:
this course is 100% fundable!

Location

Course Language
English

Training Solutions
WalkIn^®

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AI-supported gaming worlds are developing rapidly. Modern tools open up new creative avenues, automate complex steps, and expand design possibilities. This article shows how innovative ideas are gaining fresh momentum for interactive experiences with artificial intelligence.

Key topics

Adaptive mechanics through intelligent systems.
Creative content production with generative processes.
Efficient workflows for design, prototyping, and testing.
Data-driven optimization for dynamic balancing.
Current trends in automated assets and smart AI processes.

Prerequisites
A basic understanding of digital media or game-related concepts is helpful, but not essential.

Target audience
Designed for people who want to incorporate AI innovations into interactive environments – from creative professionals to those with an interest in technology.

A compact introduction that provides orientation, picks up on modern developments, and shows how artificial intelligence can give rise to new game ideas.

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course content

Introduction to AI in games

What is AI?
The development of AI in the gaming industry
Types of AI in games
Advantages, challenges, and innovations in game AI

Fundamentals of Game Design with AI

Understanding game mechanics and gameplay.
The role of AI in gameplay and narrative design.
Designing game environments for interaction with AI.
AI-driven behavior vs. traditional script logic.
Case study: Dynamic AI and narrative adaptation in "Middle-earth: Shadow of Mordor."
Practical exercise: Designing adaptive NPC behavior and interaction with the environment.

Fundamentals of AI in games

Core concepts of AI for games.
Search algorithms and pathfinding.
AI behavior modeling and procedural content generation (PCG).
Introduction to machine learning and reinforcement learning.
Case study: AI in Minecraft – procedural content generation and agent navigation.
Practical exercise: Implementation of A* pathfinding and FSM for NPC behavior.

Fundamentals of Reinforced Learning

Core concepts: states, actions, rewards, strategies, Q-learning.
Exploration versus exploitation in learning systems.
Overview of Deep Q Networks (DQN) and Policy Gradient Methods.
Case study: Reinforcement learning in DeepMind's AlphaGo.
Practical exercise: Training a model for reinforcement learning on OpenAI Gym's GridWorld.

Planning and decision-making in games

Minimax algorithm and alpha-beta pruning.
Monte Carlo tree search (MCTS).
Applications in board games and real-time strategy games (RTS).
Case study: Strategic AI in StarCraft II – Combining planning algorithms for real-time strategy games.
Practical implementation: Instructions for implementing the minimax algorithm for tic-tac-toe.

AI techniques in virtual 2D/3D gaming environments Fundamentals

Overview of 2D and 3D game environments.
Techniques for representing environments.
Navigation and pathfinding in 2D/3D spaces.
Interaction and behavior systems in virtual environments.
Case study: Navigation and interaction AI in "The Legend of Zelda: Breath of the Wild."
Practical exercise: Setting up basic navigation and interaction in 2D and 3D game environments.

Adaptive systems and dynamic difficulty level

Overview of adaptive systems.
Principles of dynamic difficulty adaptation (DDA).
Adaptive storytelling, personalization, and player profiling.
AI techniques in adaptive systems.
Implementation strategies and tools.
Case study: Dynamic enemy management and replayability with Left 4 Dead's AI Director.
Practical exercise: Development of an adaptive system for dynamic difficulty in Unity.

The future of AI in gaming

Generalist AI agents and transfer learning.
AI-supported tools for game design and testing.
Ethical considerations and AI transparency.
New technologies: VR/AR AI and AI in e-sports coaching.

Capstone project

final project

Your training will be 100% funded

Our funding schemes are available exclusively to participants who live or work in Germany. Funding is not available outside Germany.

For career starters, job seekers, career changers, startups, experienced professionals, those looking to advance their careers, specialists, etc.

The QCG program for everyone in the company

Further information

The education voucher for all job seekers

Further information

Frequently asked questions

What is the goal of AI+ Gaming™?

The course demonstrates modern AI and game frameworks such as Unity ML-Agents, TensorFlow, PyTorch, Python, and OpenAI Gym to develop intelligent game mechanics, adaptive NPCs, and data-driven player analytics.

Who benefits from AI+ Gaming™?

Ideal for creatives, developers, designers, and anyone who wants to apply and understand AI technologies in gaming in a practical way—regardless of prior knowledge.

Is prior technical knowledge required?

Not necessarily. The introduction is beginner-friendly, technical terms are explained, and the learning path is clearly structured.

How does the course help your career?

AI+ Gaming™ teaches skills that are in demand in studios and start-ups—for example, for roles in game design, AI integration, and development.

Is ethical knowledge also part of AI+ Gaming™?

Yes. The course covers fairness, responsibility, transparency, and data usage in the context of AI in gaming.

What jobs can you aim for after AI+ Gaming™?

Careers such as AI game developer, AI designer, narrative AI creator, AI animator, or indie game creator with a focus on AI are realistically achievable.

Why is AI+ Gaming™ relevant for the future?

Because AI is revolutionizing game development—with new possibilities for interaction, creativity, and efficiency. This is exactly where the course comes in.

Do you have any further questions? Please contact us.

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