1Introduction

“The whole is more than the sum of its parts.”

Aristotle

1.1 Motivation

This book is devoted to multi‐agent systems. Since this term has different meanings within different research communities, we deem it necessary to precisely define the meaning used here. In this book, a multi‐agent system refers to a network of interacting, mobile, physical entities that collectively perform a complex task beyond their individual capabilities.

Nature is replete with biological systems that fit this definition: a flock of birds, a school of fish, and a colony of insects (see Figure 1.1), to name a few. The behavior of such biological swarms is decentralized since each biological agent does not have access to global knowledge or supervision, but uses its own local sensing, decision, and control mechanisms.

Ants are a model example of a biological multi‐agent system. Ant colonies share the common goals of surviving, growing, and reproducing. Their sense of community is so strong that they behave like a single “superorganism” that can solve difficult problems by processing information as a collection 1. This collective behavior facilitates food gathering, defending nests against enemies, and building intricate structures with tunnels, chambers, and ventilation systems. Ants accomplish such feats without a supervisor telling them what to do. Rather, ant workers perform tasks based on personal aptitudes, communications with colony mates, and cues from the environment. Interactions ...

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