Course Overview
TOPThe AI+ Game Design Practitioner™ (formerly AI+ Gaming™) course explores how artificial intelligence transforms modern game design, development, and player engagement. It focuses on integrating AI into gameplay mechanics, procedural content generation, and adaptive systems. Participants gain hands-on experience implementing pathfinding, reinforcement learning, and dynamic difficulty systems. Real-world case studies from leading games illustrate how AI enhances creativity, storytelling, and interactivity.
Scheduled Classes
TOP| 07/23/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 08/20/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 09/24/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 10/22/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 11/19/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 12/17/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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What You'll Learn
TOP- Understand the role and evolution of AI in the gaming industry
- Apply machine learning and reinforcement learning techniques in game environments
- Design adaptive, data-driven, and player-personalized game systems
- Implement AI algorithms for decision-making, pathfinding, and procedural generation
- Evaluate ethical and design challenges in AI-powered games
Outline
TOPIntroduction to AI in Games
- What is AI?
- Evolution of AI in the Gaming Industry
- Types of AI in Games
- Benefits, Challenges, and Innovations in Game AI
Game Design Principles using AI
- Understanding Game Mechanics and Player Experience
- Role of AI in Gameplay and Narrative Design
- Designing Game Environments for AI Interaction
- AI-Driven Behavior vs Traditional Scripted Logic
- Case Study: Case Study: Dynamic AI and Narrative Adaptation in Middle earth: Shadow of Mordor
- Hands-On Exercise: Designing Adaptive NPC Behavior and Environment Interaction
Foundations of AI in Gaming
- Core AI Concepts for Gaming
- 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
- Hands-On: Implementing A* Pathfinding and FSM for NPC Behavior
Reinforcement Learning Fundamentals
- Core Concepts: States, Actions, Rewards, Policies, 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
- Hands-On: Train a Reinforcement Learning Model 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 (RTS) Games
- Case Study: Strategic AI in StarCraft II – Combining Planning Algorithms for Real-Time Strategy
- Hands-on Implementation: Guides on implementing the Minimax algorithm for Tic-Tac-Toe
Prerequisites
TOPRequired
- Basic Programming Skills – Comfortable with Python or similar languages.
- Foundational Math Knowledge – Understanding of linear algebra and probability.
- Intro to Machine Learning – Familiarity with ML concepts and algorithms.
- Game Development Exposure – Experience with Unity or Unreal Engine basics.
- Problem-Solving Mindset – Ability to approach challenges creatively and logically.
