Swarms systems are composed of a large number of autonomous agents, such as robots or sensors, which interact with each other and with the environment to perform a common task. A typical feature of swarm systems regards the limited knowledge of the agents, which have sensing capabilities limited to a local range around them. Therefore, each agent has access to limited information and has partial knowledge of the status of the global swarm. This is a challenge for the deployment of swarms for collective tasks, such as decision-making; while decentralised collective decision-making mechanisms are being developed, the question of how individual agents recognise in a decentralised way that a group decision has been reached, and what it is, are usually deferred. The present project aims to endow the swarm with awareness of its own state, thus allowing individual agents to reach a consensus on the global swarm state. Particular examples of states to measure are swarm size (number of agents), fraction of the swarm committed to a unique decision (quorum), and super-threshold decision (decision-state).
The challenge is to allow the swarm to reach a consensus on its own global state. Each agent can noisily estimate this state, however, different agents may disagree. To reach an agreement on the global swarm, individuals use consensus decision algorithms. We refer to decentralised sensing of swarm state as Swarm Awareness. In case of quorum sensing, this can be seen as a meta-decision, where the swarm must decide if the decision has been taken or not. During this project, we will look at solutions that social insects implements to solve similar problems; the goal will be both to better understand insects' collective behaviour and to take inspiration from nature to engineer better artificial swarm systems.
The project just started and we are seeking for a highly motivated post-doc to join the team.