Edmund Hunt

Superorganisms such as social insect colonies are very successful relative to their non-social counterparts. Powerful emergent information processing capabilities would seem to contribute to the abundance of such ‘swarm’ systems, as they effectively explore and exploit their environment collectively. We develop a Bayesian model of collective information processing in a decision-making task: choosing a nest site (a ‘multi-armed bandit’ problem).

Robot swarms can solve tasks that are impossible or too hazardous for single robots. For example, following a nuclear radiation leak, a user may wish to establish a distributed communication chain that partly extends into the most dangerous areas to gather new information. The challenge is to create long chains while maintaining chain connectivity (‘connected reach’), where those at the distant end of the chain are more likely to be disconnected.

1. Introduction Life has undergone a number of major organizational transitions, from simple self-replicating molecules into complex societies of organisms [1]. Social insects such as ants, with a reproductive division of labour between the egg-laying queen and non-reproductive workers whose genetic survival rests on her success, exemplify the highest degree of social behaviour in the animal kingdom: ‘true’ sociality or eusociality.