An individual approach to modelling emergent structure in termite swarm systems

Feltell, David; Bai, Li

doi:10.1504/ijmic.2008.018181

Cited by 12 publications

(11 citation statements)

References 8 publications

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“…The design of the agent rule sets themselves is less the focus of this paper than the algorithms to get the agents embedded in the level set environment. However, one can imagine a synthesis of concepts such as self-organisation and stigmergy from the swarm intelligence literature, but using imaging based operators as sensory input, and perhaps some artificial pheromone or other global communication medium to increase overall swarm efficiency (in this paper, the 'pheromone templates', often seen in biological swarm construction [5], can be seen as imposed by the user, rather than as emergent from agent interactions). A major benefit of this approach is that only the general area of interest within an image need be explored by the agents, effectively increasing efficiency and potentially allowing much more complex (and localised) analysis of the underlying image.…”

Section: Discussionmentioning

confidence: 99%

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3D level set image segmentation refined by intelligent agent swarm

Feltell

2010

IEEE Congress on Evolutionary Computation

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The level set method of surface representation and deformation has found many applications in image processing, especially with regard to segmentation. Naive numerical solutions have long since given way to much more efficient narrow band methods, where updates to the scalar field are performed only within a number of layers either side of the surface. This paper presents our implementation of automated segmentation via the sparse field narrow band approach. We use k-means clustering of regularly sampled points to provide initialisation of seed locations and mean voxel values, removing the need for manual seed and parameter selection. We demonstrate this method by segmenting grey matter and white matter from a simulated T1 MRI scan.Anisotropy can easily cause this and other segmentation methods to misclassify voxels, however. Often it is clear in a 3D view of the resulting surface which regions require refinement. With available extensions to the sparse field and rendering algorithms we can now efficiently perform purely localised modifications and re-rendering of the surface. These extensions, plus properties inherent in the level set method, allow us to easily situate swarms of agents on the surface, intelligently inhabiting and modifying the zero-level set. By a simple weighting of movement direction, we can direct the swarm to any user-specified point on the surface in order to affect repairs and refinement.978-1-4244-8126-2/10/$26.00 ©2010 IEEE

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Section: Discussionmentioning

confidence: 99%

“…In most models of natural swarm intelligence (for example, previous work on modelling termites [6], [5]), a random number generator is used in order to represent unseen external stochasticity. However, nature itself is entirely deterministic.…”

Section: Swarm Intelligencementioning

confidence: 99%

3D level set image segmentation refined by intelligent agent swarm

Feltell

2010

IEEE Congress on Evolutionary Computation

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“…Kandasami et al [30] studied the mechanobiological effects of cementation of bioadhesives in structures of the fungus-growing termite Odontotermes obesus . Boluses (soil-pellets [40] ) are up to 1.2 mm ball-shaped “termite bricks” formed of soil particles accumulated by individual termites [30] . Boluses include glandular secretions (saliva with cellulose digesting enzymes [109] , [169] ) and difficult to digest lignin-based phenolitic excretions [170] , [171] ), suggesting a wide range of cementation abilities symptomatic for an increased organic content of the mound soil [109] , [172] .…”

Section: Termite Structures As Functional Materialsmentioning

confidence: 99%

“…Noirot and Darlington [36] review termite nest architecture, climate regulation and defence, while Korb [37] studies similarly termite mound architecture, its function and construction with a focus on functional shapes of selected (mostly African) species. Consequently, past research was mostly concerned with autonomous nest constructions or building activities, the network structure of tunnels, and aspects of stigmergy and self-organisation [15] , [28] , [38] , [39] , [40] , [41] , [42] , [43] .…”

Section: Introduction To Termitologymentioning

confidence: 99%

“…Stigmergy , hereby defined as indirect communication [44] to exchange information through modification of the environment [40] , is a prerequisite of self-organisation and spontaneous order generation through local interactions of a seemingly erratic system [15] , [45] . Swarm behaviour or self-organisation is part of autonomous systems research with its emerging domain of swarm robotics and artificial intelligence [40] , [42] , [44] , [46] . In contrast, direct communication is provided by optical, pheromone, tactile and vibrational information [47] , [48] , [49] .…”

Section: Introduction To Termitologymentioning

confidence: 99%

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Revisiting stigmergy in light of multi-functional, biogenic, termite structures as communication channel

Oberst

Lai

Martin

et al. 2020

Computational and Structural Biotechnology Journal

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Termite mounds are fascinating because of their intriguing composition of numerous geometric shapes and materials. However, little is known about these structures, or of their functionalities. Most research has been on the basic composition of mounds compared with surrounding soils. There has been some targeted research on the thermoregulation and ventilation of the mounds of a few species of fungi-growing termites, which has generated considerable interest from human architecture. Otherwise, research on termite mounds has been scattered, with little work on their explicit properties. This review is focused on how termites design and build functional structures as nest, nursery and food storage; for thermoregulation and climatisation; as defence, shelter and refuge; as a foraging tool or building material; and for colony communication, either as in indirect communication (stigmergy) or as an information channel essential for direct communication through vibrations (biotremology). Our analysis shows that systematic research is required to study the properties of these structures such as porosity and material composition. High resolution computer tomography in combination with nonlinear dynamics and methods from computational intelligence may provide breakthroughs in unveiling the secrets of termite behaviour and their mounds. In particular, the examination of dynamic and wave propagation properties of termite-built structures in combination with a detailed signal analysis of termite activities is required to better understand the interplay between termites and their nest as superorganism. How termite structures serve as defence in the form of disguising acoustic and vibration signals from detection by predators, and what role local and global vibration synchronisation plays for building are open questions that need to be addressed to provide insights into how termites utilise materials to thrive in a world of predators and competitors.

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A New Marching Cubes Algorithm for Interactive Level Set with Application to MR Image Segmentation

Feltell

2010

Advances in Visual Computing

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An individual approach to modelling emergent structure in termite swarm systems

Cited by 12 publications

References 8 publications

3D level set image segmentation refined by intelligent agent swarm

3D level set image segmentation refined by intelligent agent swarm

Revisiting stigmergy in light of multi-functional, biogenic, termite structures as communication channel

A New Marching Cubes Algorithm for Interactive Level Set with Application to MR Image Segmentation

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