Can we build an artificial system that would be able to generate endless surprises if ran "forever" in Minecraft? While there is not a single path toward solving that grand challenge, this article presents what we believe to be some working ingredients for the endless generation of novel increasingly complex artifacts in Minecraft. Our framework for an open-ended system includes two components: a complex system used to recursively grow and complexify artifacts over time, and a discovery algorithm that leverages the concept of meta-diversity search. Since complex systems have shown to enable the emergence of considerable complexity from set of simple rules, we believe them to be great candidates to generate all sort of artifacts in Minecraft. Yet, the space of possible artifacts that can be generated by these systems is often unknown, challenging to characterize and explore. Therefore automating the long-term discovery of novel and increasingly complex artifacts in these systems is an exciting research field. To approach these challenges, we formulate the problem of meta-diversity search where an artificial "discovery assistant" incrementally learns a diverse set of representations to characterize behaviors and searches to discover diverse patterns within each of them. A successful discovery assistant should continuously seek for novel sources of diversities while being able to quickly specialize the search toward a new unknown type of diversity. To implement those ideas in the Minecraft environment, we simulate an artificial "chemistry" system based on Lenia continuous cellular automaton for generating artifacts, as well as an artificial "discovery assistant" (called Holmes) for the artifact-discovery process. Holmes incrementally learns a hierarchy of modular representations to characterize divergent sources of diversity and uses a goal-based intrinsically-motivated exploration as the diversity search strategy.

翻译：能否构建一个在Minecraft中“永续”运行时能持续产生无尽惊喜的人工系统？尽管解决这一宏大挑战尚无单一路径，本文提出了我们认为能实现Minecraft中无限生成新颖且日益复杂造物的若干可行要素。我们的开放式系统框架包含两大组件：用于随时间递归式生长与复杂化造物的复杂性系统，以及利用元多样性搜索概念的发现算法。鉴于复杂性系统已被证明能从简单规则集涌现出可观复杂性，我们认为此类系统是生成Minecraft各类造物的理想候选。然而，这些系统可能产生的造物空间往往未知、难以刻画与探索，因此自动化发现其中新颖且日益复杂造物的长期过程构成激动人心的研究领域。为应对这些挑战，我们形式化定义了元多样性搜索问题：人工“发现助手”通过增量学习多样化表征体系来刻画行为模式，并在各类表征中搜索多样性模式。成功的发现助手应能持续寻找新颖的多样性来源，同时快速将搜索聚焦于未知的新型多样性。为在Minecraft环境中实现这些构想，我们基于Lenia连续元胞自动机模拟人工“化学”系统以生成造物，并构建名为Holmes的人工“发现助手”负责造物发现。Holmes通过增量学习模块化表征层级来刻画不同来源的多样性，并采用基于目标的内在动机探索机制作为多样性搜索策略。