Applications of Multi-Agent Slime Mould Computing

TL;DR

This paper reviews a multi-agent model inspired by slime mould Physarum polycephalum, demonstrating its potential as a virtual computing material for applications like path planning and optimization.

Contribution

It introduces a novel multi-agent approach that models slime mould behaviour for unconventional computation, highlighting its emergent properties and diverse application domains.

Findings

Model exhibits self-organized network formation

Demonstrates emergent relaxation and minimization

Applicable to computational geometry and optimization

Abstract

The giant single-celled slime mould Physarum polycephalum has inspired rapid develop- ments in unconventional computing substrates since the start of this century. This is primarily due to its simple component parts and the distributed nature of the computation which it approximates during its growth, foraging and adaptation to a changing environment. Slime mould functions as a living embodied computational material which can be influenced (or pro- grammed) by the placement of external stimuli. The goal of exploiting this material behaviour for unconventional computation led to the development of a multi-agent approach to the ap- proximation of slime mould behaviour. The basis of the model is a simple dynamical pattern formation mechanism which exhibits self-organised formation and subsequent adaptation of collective transport networks. The system exhibits emergent properties such as relaxation and minimisation and it can be considered as a virtual computing material, influenced by the external application of spatial concentration gradients. In this paper we give an overview of this multi-agent approach to unconventional computing. We describe its computational mechanisms and different generic application domains, together with concrete example ap- plications of material computation. We examine the potential exploitation of the approach for computational geometry, path planning, combinatorial optimisation, data smoothing and statistical applications.