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.

Module 1: Understanding AI Agents

* 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 Module 2: Introduction to AI Game Agent

* 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 Module 3: Reinforcement Learning in Game Design

* 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 Module 4: AI for NPCs and Pathfinding

* 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 Module 5: AI for Strategic Decision-Making

* 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 Module 6: AI Game Agent in 3D Virtual Environments

* 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# Module 7: Future Trends in AI Game Design

* 7.1 Current and Future AI Trends
* 7.2 The Future of Generalist AI in Gaming
* 7.3 Case Study Module 8: Capstone Project

* 8.1. Task Description
* 8.2. Practical Implementation
* 8.3. Testing and Debugging
* 8.4. Hands-on Tools you will explore

* 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

Exam: 50 questions, 70% passing, 90 minutes, online proctored exam

Instructor-led OR Self-paced course + Official exam + Digital badge