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.

Módulos

  • Module 1: Understanding AI Agents:
    1. 1.1 What are AI Agents?
    2. 1.2 Agent Architectures and Environments
    3. 1.3 Decision Making and Behavior Basics
    4. 1.4 Introduction to Multi-Agent Systems
    5. 1.5 Case Study: Pac-Man Ghost AI
    6. 1.6 Hands On: Build a Basic Reactive AI Agent Navigating a Simple Environment Using Pygame
  • Module 2: Introduction to AI Game Agent:
    1. 2.1 What is an AI Game Agent?
    2. 2.2 Key Components of AI Game Agent
    3. 2.3 Agent Architectures
    4. 2.4 AI Game Agent Behaviors
    5. 2.5 Case Study: Racing Games (e.g., Mario Kart, Forza Horizon)
    6. 2.6 Hands-On: Creating a Simple Box Movement Game in Playcanvas
  • Module 3: Reinforcement Learning in Game Design:
    1. 3.1 Basics of Reinforcement Learning
    2. 3.2 Key Algorithms: Q-Learning and SARSA
    3. 3.3 Applying RL to Game Agents
    4. 3.4 Challenges and Solutions in Game-based RL
    5. 3.5 Case Study: AlphaZero in Games: Mastering Chess, Shogi, and Go through Self-Play and Reinforcement Learning
    6. 3.6 Hands On: Train a simple RL agent in OpenAI Gym environment
  • Module 4: AI for NPCs and Pathfinding:
    1. 4.1 Understanding NPCs as AI Agents
    2. 4.2 Simple AI Techniques for NPCs
    3. 4.3 Pathfinding Algorithms
    4. 4.4 Obstacle Avoidance and Movement Optimization
    5. 4.5 Case Study
    6. 4.6 Hands-On
  • Module 5: AI for Strategic Decision-Making:
    1. 5.1 Decision Trees and Minimax for Game AI
    2. 5.2 Monte Carlo Tree Search (MCTS) for AI Agent
    3. 5.3 Utility-Based Decision Making for Game AI
    4. 5.4 AI in Real-Time Strategy (RTS) Games
    5. 5.5 Case Study: StarCraft II AI by DeepMind
    6. 5.6 Hands-On: Implement a Basic MCTS Agent for Tic-Tac-Toe Using Pygame
  • Module 6: AI Game Agent in 3D Virtual Environments:
    1. 6.1 3D Environment Representation and Challenges for AI Agents
    2. 6.2 Navigation Mesh Generation for AI Agents in 3D
    3. 6.3 Complex Agent Behaviors in 3D Worlds
    4. 6.4 Case Study: The Last of Us
    5. 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:
    1. 7.1 Current and Future AI Trends
    2. 7.2 The Future of Generalist AI in Gaming
    3. 7.3 Case Study
  • Module 8: Capstone Project:
    1. 8.1. Task Description
    2. 8.2. Practical Implementation
    3. 8.3. Testing and Debugging
    4. 8.4. Hands-on

Herramientas de IA

  • 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
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