植物启发的AI：以植物为灵感的新型问题表述及两个案例研究

中文摘要 AI 辅助

人工智能（AI）长期以来一直受生物智能研究的启发，例如强化学习就源于动物学习研究，如今已成为解决诸多现实问题的强大范式。近期植物生物学家发现植物具备多种复杂行为，使其能灵活适应多变环境。本文提出这类行为可催生新的AI框架，涵盖监督学习、树搜索、约束满足等现有框架未关注的一系列问题。我们以植物智能解决问题的两个实例说明该思路：（1）三叶菝葜（Boquila trifoliolata）的叶拟态，这种藤本植物可改变叶片形态，同时模仿多种寄主树木的叶片；（2）根-茎协调生长，即植物在探索不同环境的器官系统间分配资源。叶拟态是三叶菝葜特有的，而根-茎协调生长在多数植物中普遍存在。针对这两个实例，我们提炼出其底层计算原理，确定了当前AI尚未解决的、符合这些框架的问题，最后概述了初步任务表述，并探讨这些表述如何应用于非植物问题。

英文摘要

Artificial Intelligence (AI) has long been inspired by studies of biological intelligence. Reinforcement learning, for instance, drew inspiration from studies involving animal learning and is now a powerful paradigm for solving many real-world problems. Recently, plant biologists have uncovered a wide range of complex behaviors in plants that enable them to flexibly adapt to variable environments. Here, we argue that such behavior can motivate new AI frameworks encompassing a range of problems overlooked by existing problem-solving frameworks such as supervised learning, tree search, and constraint satisfaction. We illustrate this idea with two examples of intelligent problem-solving in plants: (1) leaf mimicry in Boquila trifoliolata, a vine capable of altering its leaves' morphology to resemble those of multiple host trees simultaneously; and (2) coordinated root-shoot growth, wherein plants allocate resources across organ systems exploring distinct environments. While leaf mimicry is highly specific to Boquila, coordination of root-shoot growth is shared across most plants. For both examples, we capture underlying computational principles and identify problems fitting these frameworks that are currently unaddressed by AI. Finally, we outline preliminary task formulations and discuss how these formulations may be applied to non-plant problems.

发表机构

• New York University（纽约大学）
• Universidad de Murcia（穆尔西亚大学）
• University of Edinburgh（爱丁堡大学）

机构由 AI 辅助整理，请以论文原文为准。