AI+ Game Design Agent™

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

2.1 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

3.1 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

4.1 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

5.1 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

6.1 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#

7.1 Current and Future AI Trends

7.2 The Future of Generalist AI in Gaming

7.3 Case Study

8.1. Task Description

8.2. Practical Implementation

8.3. Testing and Debugging

8.4. Hands-on