Plant-Inspired AI: Plants as Inspiration for Novel Problem Formulations, and Two Case Studies

2026-08-29

AI Summary

本文通过研究植物的智能行为（如叶子模仿和根茎协调生长）来启发新的AI问题解决框架，以解决现有方法未能涵盖的问题。

Abstract

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.

Problem

• plant behavior
• novel AI frameworks
• intelligent problem-solving

Innovation

• Plant-inspired AI
• novel problem formulations
• leaf mimicry
• coordinated root-shoot growth
• computational principles