Voxelbuild
Lisa B. Soros, Justin K. Pugh, Kenneth Owen Stanley · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2017
The fields of artificial life and evolutionary robotics have seen growing interest in evolution as a source of creativity, as opposed to a tool for optimization. New intentionally divergent algorithms such as novelty search with local competition (NSLC) and MAP-Elites accordingly attempt to harness evolution's aptitude for divergence in a new search paradigm called quality diversity (QD), which aims to find a wide variety of possible solutions spread across a behavior space. To date, QD has mainly been studied in domains where potential diversity is limited. In anticipation of future, more open-ended applications of QD algorithms, this paper introduces a new domain inspired by the popular Minecraft video game featuring a larger behavior space that is substantially more difficult to exhaust. Preliminary results are presented, showcasing sample block structures built by evolved neural network controllers.