The elements of flexibility for task-performing systems

TL;DR

This paper reviews biological design features that promote flexibility and introduces a formal framework to optimize and compare flexibility in artificial systems like machine learning and robotics.

Contribution

It provides a comprehensive overview of biological flexibility elements and proposes a formalism for optimizing and understanding system flexibility across disciplines.

Findings

Identifies key biological features that enable flexibility.

Introduces a formal framework for flexibility optimization.

Facilitates interdisciplinary research and comparison.

Abstract

What makes living systems flexible so that they can react quickly and adapt easily to changing environments? This question has not only engaged biologists for decades but is also of great interest to computer scientists and engineers who seek inspiration from nature to increase the flexibility of task-performing systems such as machine learning systems, robots, or manufacturing systems. In this paper, we give a broad overview of design features of living systems that are known to promote flexibility. We call these design features the "elements of flexibility". Moreover, to facilitate interdisciplinary, bio-inspired research that brings the elements of flexibility to man-made task-performing systems, we introduce a general formalism for system flexibility optimization. The formalism is intended to (i) provide a common language to communicate ideas about system flexibility among researchers with different backgrounds, (ii) help to understand and compare existing research on system flexibility, e.g., in transfer learning or manufacturing flexibility, and (iii) provide a basis for a general theory of system flexibility optimization.