Course Prerequisites:
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.
Modules:
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 Innovation 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
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 versus 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 Real-Time Strategy (RTS) Games
5.4 Case Study: Strategic AI in StarCraft II – Combining Planning Algorithms for Real-Time Strategy
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
6.1 Overview of 2D and 3D Game Environments
6.2 Environment Representation Techniques
6.3 Navigation and Pathfinding in 2D/3D Spaces
6.4 Interaction and Behavior Systems in Virtual Environments
6.5 Case Study: Navigation and Interaction AI in The Legend of Zelda: Breath of the Wild - – Enhancing
6.6 Hands-on: Building Basic Navigation and Interaction in 2D and 3D Game 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: Adaptive Difficulty in Resident Evil 4
7.7 Hands-on: Build a Simple Adaptive 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 in Gaming
8.4 Emerging Technologies: VR/AR AI and AI in Esports Coaching
Module 9: Capstone Project
9.1 Generalist AI Agents and Transfer Learning