  We explore the generation of diverse environments using the Amorphous
Fortress (AF) simulation framework. AF defines a set of Finite State Machine
(FSM) nodes and edges that can be recombined to control the behavior of agents
in the `fortress' grid-world. The behaviors and conditions of the agents within
the framework are designed to capture the common building blocks of multi-agent
artificial life and reinforcement learning environments. Using quality
diversity evolutionary search, we generate diverse sets of environments. These
environments exhibit certain types of complexity according to measures of
agents' FSM architectures and activations, and collective behaviors. Our
approach, Quality Diversity in Amorphous Fortress (QD-AF) generates families of
0-player games akin to simplistic ecological models, and we identify the
emergence of both competitive and co-operative multi-agent and multi-species
survival dynamics. We argue that these generated worlds can collectively serve
as training and testing grounds for learning algorithms.
