AI+ Gaming™

Discover how AI transforms game design, player engagement, and virtual environments. Build real-world gaming projects using cutting-edge AI technologies.
  • Comprehensive Skill Development
    Master AI-driven game design, adaptive storytelling, and intelligent NPC development to create immersive, data-enhanced gaming experiences.
  • Industry Recognition
    Earn a globally recognized certification that validates your expertise in integrating artificial intelligence within modern gaming environments.
  • Hands-On Learning
    Work on real-world gaming projects, from AI-based character behavior modeling to predictive player analytics, enhancing creativity and technical precision.
  • Career Advancement
    Unlock career opportunities in game development, AI simulation design, virtual production, and interactive entertainment industries.
  • Future-Ready Expertise
    Stay at the forefront of gaming innovation with cutting-edge knowledge in generative AI, immersive simulations, and intelligent gameplay systems.

Módulos

  • Module 1: Introduction to AI in Games:
    1. 1.1 What is AI?
    2. 1.2 Evolution of AI in the Gaming Industry
    3. 1.3 Types of AI in Games
    4. 1.4 Benefits, Challenges, and Innovations in Game AI
  • Module 2: Game Design Principles using AI:
    1. 2.1 Understanding Game Mechanics and Player Experience
    2. 2.2 Role of AI in Gameplay and Narrative Design
    3. 2.3 Designing Game Environments for AI Interaction
    4. 2.4 AI-Driven Behavior vs Traditional Scripted Logic
    5. 2.5 Case Study: Dynamic AI and Narrative Adaptation in Middle earth: Shadow of Mordor
    6. 2.6 Hands-On Exercise: Designing Adaptive NPC Behavior and Environment Interaction
  • Module 3: Foundations of AI in Gaming:
    1. 3.1 Core AI Concepts for Gaming
    2. 3.2 Search Algorithms and Pathfinding
    3. 3.3 AI Behavior Modeling and Procedural Content Generation (PCG)
    4. 3.4 Introduction to Machine Learning and Reinforcement Learning
    5. 3.5 Case Study: AI in Minecraft — Procedural Content Generation and Agent Navigation
    6. 3.6 Hands-On: Implementing A* Pathfinding and FSM for NPC Behavior
  • Module 4: Reinforcement Learning Fundamentals:
    1. 4.1 Core Concepts: States, Actions, Rewards, Policies, Q-Learning:
    2. 4.2 Exploration versus Exploitation in Learning Systems:
    3. 4.3 Overview of Deep Q Networks (DQN) and Policy Gradient Methods
    4. 4.4 Case Study: Reinforcement Learning in DeepMind’s AlphaGo
    5. 4.5 Hands-On: Train a Reinforcement Learning Model on OpenAI Gym’s GridWorld
  • Module 5: Planning and Decision Making in Games:
    1. 5.1 Minimax Algorithm and Alpha-Beta Pruning
    2. 5.2 Monte Carlo Tree Search (MCTS)
    3. 5.3 Applications in Board Games and Real-Time Strategy (RTS) Games
    4. 5.4 Case Study: Strategic AI in StarCraft II – Combining Planning Algorithms for Real-Time Strategy
    5. 5.5 Hands-on Implementation: Guides on implementing the Minimax algorithm for Tic-Tac-Toe
  • Module 6: AI Techniques in 2D/3D Virtual Gaming Environments Basic:
    1. 6.1 Overview of 2D and 3D Game Environments
    2. 6.2 Environment Representation Techniques
    3. 6.3 Navigation and Pathfinding in 2D/3D Spaces
    4. 6.4 Interaction and Behavior Systems in Virtual Environments
    5. 6.5 Case Study: Navigation and Interaction AI in The Legend of Zelda: Breath of the Wild
    6. 6.6 Hands-On: Building Basic Navigation and Interaction in 2D and 3D Game Environments
  • Module 7: Adaptive Systems and Dynamic Difficulty:
    1. 7.1 Adaptive Systems Overview
    2. 7.2 Dynamic Difficulty Adjustment (DDA) Principles
    3. 7.3 Adaptive Storytelling, Personalization, and Player Profiling
    4. 7.4 AI Techniques in Adaptive Systems
    5. 7.5 Implementation Strategies and Tools
    6. 7.6 Case Study: Dynamic Enemy Management and Replayability with Left 4 Dead’s AI Director
    7. 7.7 Hands-On: Developing an Adaptive Dynamic Difficulty System in Unity
  • Module 8: Future of AI in Gaming:
    1. 8.1 Generalist AI Agents and Transfer Learning
    2. 8.2 AI-Powered Game Design and Testing Tools
    3. 8.3 Ethical Considerations and AI Transparency
    4. 8.4 Emerging Technologies: VR/AR AI and AI in Esports Coaching
  • Module 9: Capstone Project:

Herramientas de IA

  • Unity ML-Agents
  • TensorFlow
  • PyTorch
  • Python
  • OpenAI Gym
  • Blender
  • NVIDIA DeepStream
  • Reinforcement Learning Frameworks
  • Natural Language Processing Libraries
  • Computer Vision SDKs
  • Game Data Analytics Tools
  • Behavior Tree Editors
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