How robots in a large group make decisions as a whole? From biological inspiration to the design of distributed algorithms

TL;DR

This paper reviews how natural collective decision-making processes inspire the design of distributed algorithms in engineering, emphasizing robustness, flexibility, and scalability, supported by mathematical models.

Contribution

It introduces a modular framework for designing distributed decision algorithms inspired by biological systems, with theoretical guarantees and extendability.

Findings

Biological systems use simple local interactions for decision-making.

Engineered algorithms mimic natural processes with performance guarantees.

The framework supports modular, extendable, and mathematically modeled decision algorithms.

Abstract

Nature provides us with abundant examples of how large numbers of individuals can make decisions without the coordination of a central authority. Social insects, birds, fishes, and many other living collectives, rely on simple interaction mechanisms to do so. They individually gather information from the environment; small bits of a much larger picture that are then shared locally among the members of the collective and processed together to output a commonly agreed choice. Throughout evolution, Nature found solutions to collective decision-making problems that are intriguing to engineers for their robustness to malfunctioning or lost individuals, their flexibility in face of dynamic environments, and their ability to scale with large numbers of members. In the last decades, whereas biologists amassed large amounts of experimental evidence, engineers took inspiration from these and other examples to design distributed algorithms that, while maintaining the same properties of their natural counterparts, come with guarantees on their performance in the form of predictive mathematical models. In this paper, we review the fundamental processes that lead to a collective decision. We discuss examples of collective decisions in biological systems and show how similar processes can be engineered to design artificial ones. During this journey, we review a framework to design distributed decision-making algorithms that are modular, can be instantiated and extended in different ways, and are supported by a suit of predictive mathematical models.