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 Innovations 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
- 2.5 Case Study: Dynamic AI and Narrative Adaptation in Middle earth: Shadow of Mordor
- 2.6 Hands-On Exercise: Designing Adaptive NPC Behavior and Environment Interaction
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
- 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: Dynamic Enemy Management and Replayability with Left 4 Dead’s AI Director
- 7.7 Hands-On: Developing an Adaptive Dynamic 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
- 8.4 Emerging Technologies: VR/AR AI and AI in Esports Coaching
Module 9: Capstone Project
Tools you will explore
- 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
