用于形态发生系统探索性搜索的层次组织潜在模块
Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic Systems
摘要：
在许多自然和人工系统中，由局部相互作用形成的复杂形态模式的自组织是一个令人着迷的现象。在人工世界中，这种形态发生系统的典型例子是细胞自动机。然而，它们的机制往往很难掌握，到目前为止，新模式的科学发现主要依赖于手动调整和特设的探索性搜索。最近引入了这些系统中的自动多样性驱动发现问题[26，62]，强调两个关键要素是自主探索和无监督表示学习，以描述模式中的“相关”变化程度。在本文中，我们激励需要我们所谓的元多样性搜索，认为没有一个独特的地面真理有趣的多样性，因为它强烈依赖于最终的观察者和它的动机。使用一个连续的生命游戏系统进行实验，我们提供的经验证据表明，依赖于单片架构的行为嵌入设计倾向于偏见的最终发现（无论是手工定义和无监督学习的功能），这是不太可能与最终用户的兴趣。为了解决这些问题，我们引入了一种新的动态和模块化的架构，使无监督学习的层次结构的不同表示。结合内在动机的目标探索算法，我们表明，该系统形成了一个发现助手，可以有效地适应其多样性搜索对用户的偏好，只使用非常少量的用户反馈。
英文摘要：
Self-organization of complex morphological patterns from local interactions is a fascinating phenomenon in many natural and artificial systems. In the artificial world, typical examples of such morphogenetic systems are cellular automata. Yet, their mechanisms are often very hard to grasp and so far scientific discoveries of novel patterns have primarily been relying on manual tuning and ad hoc exploratory search. The problem of automated diversity-driven discovery in these systems was recently introduced [26, 62], highlighting that two key ingredients are autonomous exploration and unsupervised representation learning to describe "relevant" degrees of variations in the patterns. In this paper, we motivate the need for what we call Meta-diversity search, arguing that there is not a unique ground truth interesting diversity as it strongly depends on the final observer and its motives. Using a continuous game-of-life system for experiments, we provide empirical evidences that relying on monolithic architectures for the behavioral embedding design tends to bias the final discoveries (both for hand-defined and unsupervisedly-learned features) which are unlikely to be aligned with the interest of a final end-user. To address these issues, we introduce a novel dynamic and modular architecture that enables unsupervised learning of a hierarchy of diverse representations. Combined with intrinsically motivated goal exploration algorithms, we show that this system forms a discovery assistant that can efficiently adapt its diversity search towards preferences of a user using only a very small amount of user feedback.
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。
