MMO Simulator Documentationο
Welcome to MMO Simulator, a multi-agent simulation framework for studying emergent behavior and economic patterns in autonomous agent systems.
Overviewο
MMO Simulator is a research framework inspired by games like Dwarf Fortress, designed to simulate autonomous agents that:
πΊοΈ Explore procedurally-generated 2D worlds with diverse terrain types
πͺ Gather resources (wood, stone, herbs, fish) using appropriate tools
βοΈ Combat hostile NPCs and collect loot
π οΈ Craft items from gathered materials using recipes
π° Trade with other agents in an emergent market economy
π§ Decide autonomously using personality-driven AI and utility-based goal selection
The framework logs all actions, combat, trades, and entity states to a SQLite database for post-simulation analysis of economic trends, resource distribution, and behavioral patterns.
Key Featuresο
- Flexible Architecture
Object-oriented design with extensible base classes for entities, actions, and items. Add new content through database definitions without code changes.
- Personality-Driven AI
Agents have unique personalities (curiosity, bravery, sociability, greed, patience) and character classes (Warrior, Mage, Hunter, Alchemist, etc.) that influence their decision-making.
- Goal-Based Behavior
Utility-based AI system where agents autonomously select and pursue goals like exploring, gathering resources, crafting items, attacking enemies, or trading.
- Emergent Economy
Non-blocking trading system where agents post offers and form trade networks based on supply and demand.
- Comprehensive Analytics
All simulation data logged to SQLite for analysis of economic patterns, social networks, and long-term adaptation.
- Real-time Visualization
Optional Pygame-based visualization showing agent movements, combat, resource gathering, and world state.
Quick Startο
# Clone the repository
git clone https://github.com/AoifeHughes/MMO_Simulator.git
cd MMO_Simulator
# Install dependencies
pip install -r requirements.txt
# Run a basic simulation
python examples/complex_simulation.py --agents 10 --npcs 5 --ticks 180
See Installation for detailed installation instructions and Quick Start Guide for your first simulation.
Documentation Structureο
Getting Started
API Reference
Architecture
Research Applicationsο
This framework is designed for research into:
Emergent Economics: How do market prices, trade networks, and resource distribution emerge from individual agent decisions?
Behavioral Adaptation: How do agents with different personalities and classes specialize and adapt over time?
Social Dynamics: What relationship patterns and cooperation strategies develop?
Resource Management: How efficiently do agents discover, extract, and utilize resources?
Contributingο
Contributions are welcome! Please see the GitHub repository for contribution guidelines.
Licenseο
This project is licensed under the MIT License. See the LICENSE file for details.