Sociogenesis and Collective Movement

: An interdisciplinary approach connecting statistical physics, animal ecology and artificial intelligence

Abstract

The collective robotics task of distributed spatial coverage through random motion is inherently difficult in unbounded environments. In the absence of boundaries, the diffusion of agents is an unavoidable result of stochastic movement that leads to a breaking up of the group. This thesis initiates an investigation of the ways in which the intelligence of individual agents can be used to produce cohesive collective random motion in unbounded environments. A perceptual computation framework is developed. This reveals that when agents are able to model and respond to the system which they are part of, increasingly sophisticated collective behaviour is produced.

This framework is used to develop a swarm robotics sociogenesis solution, taking inspiration from the spatial sorting of animal groups according to social dominance hierarchies. Agents are co-ordinated in space using a self-organised artificial social structure, by integrating distributed machine learning methods into the swarm intelligence paradigm. Each agent learns a local regression model of the artificial social structure, and moves with the goal of minimising the difference between its own state and that predicted by its internal model. A one-dimensional sociogenesis movement model is developed and tested in simulation, and achieves self-organised and cohesive spatial coverage. Following this, the model is extended to two-dimensional space and achieves excellent spatial sorting that remains stable up to intermediate simulation times.

The final part of this thesis investigates how the motion of single agents becomes bounded through a memory of past occupied states. The influence of memory is formally studied using a generalised Langevin equation (GLE). A set of Fokker-Planck equations is derived to describe the macroscopic properties of the GLE, extending previously derived results. The exact conditions for which the memory of the agent produces non-Markov dynamics are then derived, using the conditional probability of the process.

The work presented in chapter 2 is being prepared for submission in Collective Intelligence: Z. Neu and L. Giuggioli, Towards a statistical physics of interacting intelligent agents, 2022. The results of chapter 3 are being prepared for submission in the journal paper Physical Review E: Z. Neu and L. Giuggioli, A sociogenesis movement model for cohesive collective motion in unbounded domains, 2022, and have also resulted in a research grant from Amazon Research Awards to develop the project Multi-robot online exploration in extreme unbounded environments through adaptive socio-spatial ordering, 2022-2023.

Date of Award	2 Dec 2021
Original language	English
Awarding Institution	University of Bristol
Supervisor	Luca Giuggioli (Supervisor) & Sabine Hauert (Supervisor)