AI+ Gaming (AP-6011 Self-Paced Training)
Ziele der Schulung
The AI+ Gaming certification equips learners with the skills to integrate artificial intelligence into modern game design and development. It helps participants master AI-driven game mechanics, adaptive storytelling, intelligent NPC behavior, procedural content generation, and player behavior analytics. The course is tailored for those who want to build real-world gaming projects using cutting-edge AI technologies and unlock career opportunities in interactive entertainment, simulation design, and game development industries. What Will You Learn? You will learn how to integrate AI into gameplay mechanics, use procedural generation to create dynamic worlds, analyze player data to enhance engagement, implement reinforcement learning for intelligent NPCs, and apply AI models in engines like Unity and Unreal for real projects.
Zielgruppe Seminar
- Aspiring Game Developers – ideal for integrating AI into game design and development.
- AI Enthusiasts – great for those curious about AI’s role in gaming experiences.
- Game Designers – suited for creatives using AI for storytelling, adaptive worlds, and gameplay.
- Software Engineers – professionals applying programming and AI within games.
- Students & Researchers – those pursuing studies in AI, machine learning, or interactive entertainment.
Voraussetzungen
- Basic programming knowledge in Python.
- Understanding of linear algebra and probability.
- Familiarity with machine learning principles
- Experience with Unity or Unreal Engine.
- Creative problem-solving mindset.
Seminarinhalt
Module 1: Introduction to AI in Games
- 1.1 What is AI
- 1.2 Evolution of AI in the Gaming Industry
- 1.3 Types of AI in Games
- 1.4 Benefits, Challenges, and Innovations in Game AI
Module 2: Game Design Principles using AI
- 2.1 Understanding Game Mechanics and Player Experience
- 2.2 Role of AI in Gameplay and Narrative Design
- 2.3 Designing Game Environments for AI Interaction
- 2.4 AI-Driven Behavior vs Traditional Scripted Logic
- 2.5 Case Study: Dynamic AI and Narrative Adaptation in Middle-earth: Shadow of Mordor
- 2.6 Hands-On Exercise: Designing Adaptive NPC Behavior and Environment Interaction
Module 3: Foundations of AI in Gaming
- 3.1 Core AI Concepts for Gaming
- 3.2 Search Algorithms and Pathfinding
- 3.3 AI Behavior Modeling and Procedural Content Generation (PCG)
- 3.4 Introduction to Machine Learning and Reinforcement Learning
- 3.5 Case Study: AI in Minecraft — Procedural Content Generation and Agent Navigation
- 3.6 Hands-On: Implementing A* Pathfinding and FSM for NPC Behavior
Module 4: Reinforcement Learning Fundamentals
- 4.1 Core Concepts: States, Actions, Rewards, Policies, Q-Learning
- 4.2 Exploration vs. Exploitation in Learning Systems
- 4.3 Overview of Deep Q Networks (DQN) and Policy Gradient Methods
- 4.4 Case Study: Reinforcement Learning in DeepMind’s AlphaGo
- 4.5 Hands-On: Train a Reinforcement Learning Model on OpenAI Gym’s GridWorld
Module 5: Planning and Decision Making in Games
- 5.1 Minimax Algorithm and Alpha-Beta Pruning
- 5.2 Monte Carlo Tree Search (MCTS)
- 5.3 Applications in Board Games and RTS Games
- 5.4 Case Study: Strategic AI in StarCraft II
- 5.5 Hands-On Implementation: Tic-Tac-Toe Minimax Algorithm
Module 6: AI Techniques in 2D/3D Virtual Gaming Environments Basic
- 6.1 Overview of 2D/3D Game Environment
- 6.2 Environment Representation Techniques
- 6.3 Navigation and Pathfinding in 2D/3D Spaces
- 6.4 Interaction and Behavior Systems
- 6.5 Case Study: Navigation AI in The Legend of Zelda: Breath of the Wild
- 6.6 Hands-On: Basic Navigation & Interaction in Virtual Environments
Module 7: Adaptive Systems and Dynamic Difficulty
- 7.1 Adaptive Systems Overview
- 7.2 Dynamic Difficulty Adjustment (DDA) Principles
- 7.3 Adaptive Storytelling, Personalization, and Player Profiling
- 7.4 AI Techniques in Adaptive Systems
- 7.5 Implementation Strategies and Tools
- 7.6 Case Study: Dynamic Enemy Management and Replayability with Left 4 Dead’s AI Director
- 7.7 Hands-On: Developing an Adaptive Dynamic Difficulty System in Unity
Module 8: Future of AI in Gaming
- 8.1 Generalist AI Agents and Transfer Learning
- 8.2 AI-Powered Game Design and Testing Tools
- 8.3 Ethical Considerations and AI Transparency
- 8.4 Emerging Technologies: VR/AR AI and AI in Esports Coaching ###Module 9: Capstone Project
Hinweise
Prüfung und Zertifizierung
- Exam Format: 50 questions, ~70% passing score required.
- Duration: 90 minutes.
- Delivery Method: Online proctored exam.
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Übersicht: AI Certs Schulungen Portfolio
