# Amorphous Fortress: Observing Emergent Behavior in Multi-Agent FSMs

arXiv: https://arxiv.org/abs/2306.13169 · HTML: https://mlsanity.com/paper/2306.13169/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 22 Jun 2023.

## Builds on (most cited first, 6 of 6)

- [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015)
- [Using Fractal Neural Networks to Play SimCity 1 and Conway's Game of Life at Variable Scales](https://mlsanity.com/paper/2002.03896/) (2020)
- [An Ontology-based Adaptive Personalized E-learning System, Assisted by Software Agents on Cloud Storage](https://mlsanity.com/paper/1709.01492/) (2017)
- [Say "Sul Sul!" to SimSim, A Sims-Inspired Platform for Sandbox Game AI](https://mlsanity.com/paper/2008.11258/) (2020)
- [Exploring open-ended gameplay features with Micro RollerCoaster Tycoon](https://mlsanity.com/paper/2105.04342/) (2021)
- ['That Darned Sandstorm': A Study of Procedural Generation through Archaeological Storytelling](https://mlsanity.com/paper/2304.08293/) (2023)

## Built on by (most cited first, 3 of 3)

- [From Text to Life: On the Reciprocal Relationship between Artificial Life and Large Language Models](https://mlsanity.com/paper/2407.09502/) (2024)
- [JaxLife: An Open-Ended Agentic Simulator](https://mlsanity.com/paper/2409.00853/) (2024)
- [Quality Diversity in the Amorphous Fortress (QD-AF): Evolving for Complexity in 0-Player Games](https://mlsanity.com/paper/2312.02231/) (2023)

## Abstract (arXiv)

We introduce a system called Amorphous Fortress -- an abstract, yet spatial, open-ended artificial life simulation. In this environment, the agents are represented as finite-state machines (FSMs) which allow for multi-agent interaction within a constrained space. These agents are created by randomly generating and evolving the FSMs; sampling from pre-defined states and transitions. This environment was designed to explore the emergent AI behaviors found implicitly in simulation games such as Dwarf Fortress or The Sims. We apply the hill-climber evolutionary search algorithm to this environment to explore the various levels of depth and interaction from the generated FSMs.