Procedural Content Generation with AI Full Syllabus

Module 1: Introduction to Procedural Content Generation (PCG)

• What is Procedural Content Generation?
• History & evolution of PCG in games, art, and simulations
• Procedural vs manually created content
• Role of AI in enhancing PCG

Module 2: Foundations of PCG Techniques

• Rule-based generation (tile sets, grammars)
• Noise functions (Perlin, Simplex, Value)
• Fractals and L-systems
• Random vs pseudo-random generation
• Deterministic vs non-deterministic generation

Module 3: AI in PCG – Techniques & Models

• Neural networks in PCG
• Evolutionary algorithms (genetic algorithms, NEAT)
• Reinforcement Learning for dynamic content
• GANs (Generative Adversarial Networks) for visuals
• Transformers & LLMs for narrative/content generation

Module 4: Procedural Level Design

• Algorithms for dungeon generation (BSP trees, cellular automata)
• 2D & 3D terrain generation using noise + AI
• Platformer/roguelike layout generation
• Ensuring playability, challenge scaling using AI

Module 5: Procedural Art and Visual Assets

• AI image generators (DALL·E, Midjourney, Stable Diffusion) for textures, tiles, patterns
• Style transfer and AI upscaling
• Procedural material generation (using Substance Designer, Blender + AI)
• Infinite texture + variation generation using latent diffusion models

Module 6: Storytelling & Dialogue Generation

• Generating quest lines, branching narratives with GPT/LLMs
• World lore, backstories using AI (Chain-of-thought prompting)
• NPC dialogues and behavior trees using AI
• Consistency & memory in long-term storytelling

Module 7: Object and Asset Generation

• Procedural generation of:
  • Weapons
  • Vehicles
  • Buildings
  • Enemies / Creatures
• Parameterized design: how sliders & AI combine for variations
• Using AI to create asset metadata (rarity, power, traits)

Module 8: Environments and Ecosystems

• Biome generation using climate simulation + ML
• Procedural flora and fauna
• AI-based environmental storytelling (ruins, camps, trails)
• Weather + lighting systems powered by procedural logic

Module 9: AI-Driven Testing & Optimization

• Using AI to test generated content:
  • Pathfinding, logic testing, player feedback simulation
• Detecting dead-ends, unbalanced levels, visual glitches
• Auto-balancing difficulty using reinforcement learning
• QA automation using PCG + AI agents

Module 10: Toolchain Integration & Automation

• Integration with:
  • Unity (C#, Procedural Toolkit, Gaia, etc.)
  • Unreal Engine (Blueprints + Python AI tools)
  • Blender + Python + AI APIs
• Real-time generation in runtime environments
• Saving and reusing AI-generated content structures