Evolving Flying Machines in Minecraft Using Quality Diversity

TL;DR

This paper demonstrates that quality diversity algorithms, specifically MAP-Elites, can reliably evolve flying machines in Minecraft, surpassing traditional fitness-based methods in discovering diverse functional designs.

Contribution

It introduces the application of MAP-Elites for evolving flying machines in Minecraft, highlighting its effectiveness over fitness-only approaches.

Findings

MAP-Elites reliably discovers flying machines in Minecraft.

A sophisticated fitness function improves evolution success.

Behavior characterization guides diverse solution discovery.

Abstract

Minecraft is a great testbed for human creativity that has inspired the design of various structures and even functioning machines, including flying machines. EvoCraft is an API for programmatically generating structures in Minecraft, but the initial work in this domain was not capable of evolving flying machines. This paper applies fitness-based evolution and quality diversity search in order to evolve flying machines. Although fitness alone can occasionally produce flying machines, thanks in part to a more sophisticated fitness function than was used previously, the quality diversity algorithm MAP-Elites is capable of discovering flying machines much more reliably, at least when an appropriate behavior characterization is used to guide the search for diverse solutions.