AI+ Game Design Agent

Prerequisites

• Basic Programming Knowledge: Familiarity with coding concepts and languages.
• Game Design Fundamentals: Understanding of core game mechanics and structure.
• Mathematics and Algorithms: Strong grasp of logic and problem-solving techniques.
• Artificial Intelligence Basics: Introductory knowledge of AI principles and models.
• Creative Thinking: Ability to envision dynamic and interactive game elements.

Course outline

1. 1

   Lesson 1: Understanding AI Agents

   • 1.1 What are AI Agents?
   • 1.2 Agent Architectures and Environments
   • 1.3 Decision Making and Behavior Basics
   • 1.4 Introduction to Multi-Agent Systems
   • 1.5 Case Study: Pac-Man Ghost AI
   • 1.6 Hands On: Build a Basic Reactive AI Agent Navigating a Simple Environment Using Pygame
2. 2

   Lesson 2: Introduction to AI Game Agent

   • 2.1 What is an AI Game Agent?
   • 2.2 Key Components of AI Game Agent
   • 2.3 Agent Architectures
   • 2.4 AI Game Agent Behaviors
   • 2.5 Case Study: Racing Games (e.g., Mario Kart, Forza Horizon)
   • 2.6 Hands-On: Creating a Simple Box Movement Game in Playcanvas
3. 3

   Lesson 3: Reinforcement Learning in Game Design

   • 3.1 Basics of Reinforcement Learning
   • 3.2 Key Algorithms: Q-Learning and SARSA
   • 3.3 Applying RL to Game Agents
   • 3.4 Challenges and Solutions in Game-based RL
   • 3.5 Case Study: AlphaZero in Games: Mastering Chess, Shogi, and Go through Self-Play and Reinforcement Learning
   • 3.6 Hands On: Train a simple RL agent in OpenAI Gym environment
4. 4

   Lesson 4: AI for NPCs and Pathfinding

   • 4.1 Understanding NPCs as AI Agents
   • 4.2 Simple AI Techniques for NPCs
   • 4.3 Pathfinding Algorithms
   • 4.4 Obstacle Avoidance and Movement Optimization
   • 4.5 Case Study
   • 4.6 Hands-On
5. 5

   Lesson 5: AI for Strategic Decision-Making

   • 5.1 Decision Trees and Minimax for Game AI
   • 5.2 Monte Carlo Tree Search (MCTS) for AI Agent
   • 5.3 Utility-Based Decision Making for Game AI
   • 5.4 AI in Real-Time Strategy (RTS) Games
   • 5.5 Case Study: StarCraft II AI by DeepMind
   • 5.6 Hands-On: Implement a Basic MCTS Agent for Tic-Tac-Toe Using Pygame
6. 6

   Lesson 6: AI Game Agent in 3D Virtual Environments

   • 6.1 3D Environment Representation and Challenges for AI Agents
   • 6.2 Navigation Mesh Generation for AI Agents in 3D
   • 6.3 Complex Agent Behaviors in 3D Worlds
   • 6.4 Case Study: The Last of Us
   • 6.5 Hands On: Develop a 3D AI Agent with Navigation and Interaction in Unity Using NavMesh and C#
7. 7

   Lesson 7: Future Trends in AI Game Design

   • 7.1 Current and Future AI Trends
   • 7.2 The Future of Generalist AI in Gaming
   • 7.3 Case Study
8. 8

   Lesson 8: Capstone Project

   • 8.1. Task Description
   • 8.2. Practical Implementation
   • 8.3. Testing and Debugging
   • 8.4. Hands-on