在连续元胞自动机的相边界处寻找复杂性
Looking for Complexity at Phase Boundaries in Continuous Cellular Automata
AI总结：
针对人工生命中连续系统高维参数空间内复杂行为难寻的问题，提出PTF算法定位相边界参数，应用于Lenia可将有趣行为频率提升两倍以上且支持大规模搜索。
摘要：
人工生命领域的一项核心挑战是设计能涌现出复杂行为的系统。这类系统多依赖高维参数空间，其中仅很小一部分子空间会呈现出有趣的动力学特性。针对连续系统场景，我们提出了“相变查找器”（Phase Transition Finder，PTF）算法，该算法可高效生成处于两个相之间边界上的参数。我们认为这类点更有可能展现出复杂行为，并通过将PTF应用于Lenia系统验证了这一点：实验表明该算法可将有趣行为的出现频率提升两倍以上，同时效率足以支撑大规模搜索。
英文摘要：
One key challenge in Artificial Life is designing systems that display an emergence of complex behaviors. Many such systems depend on a high-dimensional parameter space, only a small subset of which displays interesting dynamics. Focusing on the case of continuous systems, we introduce the 'Phase Transition Finder'(PTF) algorithm, which can be used to efficiently generate parameters lying at the border between two phases. We argue that such points are more likely to display complex behaviors, and confirm this by applying PTF to Lenia showing it can increase the frequency of interesting behaviors more than two-fold, while remaining efficient enough for large-scale searches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
