From Pattern Formation to Material Computation: Multi-agent Modelling of Physarum Polycephalum

369 Pages·2015·62.89 MB·English

by  Jeff Jones (auth.)

Emergence, Complexity and Computation ECC
Jeff  Jones
From Pattern
Formation
to Material
Computation
Multi-agent Modelling of Physarum
Polycephalum
Emergence, Complexity and Computation
Volume 15
Serieseditors
IvanZelinka,TechnicalUniversityofOstrava,Ostrava,CzechRepublic
e-mail:[e-mail]
AndrewAdamatzky,UniversityoftheWestofEngland,Bristol,UnitedKingdom
e-mail:[e-mail]
GuanrongChen,CityUniversityofHongKong,HongKong
e-mail:[e-mail]
EditorialBoard
AjithAbraham,MirLabs,USA
AnaLuciaC.Bazzan,UniversidadeFederaldoRioGrandedoSul,PortoAlegre
RSBrasil
JuanC.Burguillo,UniversityofVigo,Spain
SergejCˇelikovský,AcademyofSciencesoftheCzechRepublic,CzechRepublic
MohammedChadli,UniversityofJulesVerne,France
EmilioCorchado,UniversityofSalamanca,Spain
DonaldDavendra,TechnicalUniversityofOstrava,CzechRepublic
AndrewIlachinski,CenterforNavalAnalyses,USA
JouniLampinen,UniversityofVaasa,Finland
MartinMiddendorf,UniversityofLeipzig,Germany
EdwardOtt,UniversityofMaryland,USA
LinqiangPan,HuazhongUniversityofScienceandTechnology,Wuhan,China
GheorghePa˘un,RomanianAcademy,Bucharest,Romania
HendrikRichter,HTWKLeipzigUniversityofAppliedSciences,Germany
JuanA.Rodriguez-Aguilar,IIIA-CSIC,Spain
OttoRössler,InstituteofPhysicalandTheoreticalChemistry,Tübingen,Germany
VaclavSnasel,TechnicalUniversityofOstrava,CzechRepublic
IvoVondrák,TechnicalUniversityofOstrava,CzechRepublic
HectorZenil,KarolinskaInstitute,Sweden
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anditsimpactandlimitstoanycomputingbasedonphysicallimits(thermodynamic
and quantumlimits, Bremermann’slimit, Seth Lloyd limits...) as well as algorith-
mic limits (Gödel’s proof and its impact on calculation, algorithmic complexity,
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Jeff Jones
From Pattern Formation
to Material Computation
Multi-agent Modelling of Physarum
Polycephalum
ABC
JeffJones
UnconventionalComputingCentre
UniversityoftheWestofEngland
Bristol
UnitedKingdom
ISSN2194-7287 ISSN2194-7295 (electronic)
Emergence,ComplexityandComputation
ISBN978-3-319-16822-7 ISBN978-3-319-16823-4 (eBook)
DOI10.1007/978-3-319-16823-4
LibraryofCongressControlNumber:2015937729
SpringerChamHeidelbergNewYorkDordrechtLondon
(cid:2)c SpringerInternationalPublishingSwitzerland2015
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For Wendy
Preface
This book describes simple multi-agent mechanisms which model the be-
haviour of an extraordinary creature, the true slime mould Physarum Poly-
cephalum. This giant (up to square metre sized) single-celled organism was
onceknownonlyforitscuriousbiologicalpropertiesandbehaviour.Sincethe
start of this 21st Century, however, it has been the subject of a wide range
of studies into its computational abilities. Physarum is both fascinating and
puzzling because it can perform such complex biological and computational
feats without a brain, or indeed any specialised nervous tissue. It has since
becomethesubjectofintensiveresearchinbothclassicalandunconventional
computing and robotics research.
To model the behaviour of slime mould in this book, we describe and ex-
plorethe complexpatternformationthatemergesfrominteractionswithina
populationofverysimplemobileagents.Theagentsthemselvesaresosimple
and generic that they may be regarded as particles, yet their interactions
yield surprisingly complex emergent patterns. An evaluation of the pattern
formationproducedbytheseparticleinteractionsisdescribed,alongwiththe
effectsofkeymodelparameters.Weshowthatthesepatternsexhibitsecond-
order behaviours which approximate phenomena observed in physical sys-
tems, including self-organised network assembly and network minimisation.
We use these emergent and quasi-physical pattern formation mechanisms as
the basis of a simple bottom-up model of Physarum slime mould. We re-
produce its biological behaviour, demonstrating how Physarum may offload
somecomputationtotheenvironmentinaparsimonioustwo-waymechanism
of sensing — and subsequently modifying — spatial diffusion gradients.
Wethenshowhowthisvirtualslimemould(aswithrealslimemould)canbe
consideredasamaterial-based,spatiallyrepresented,unconventionalcomput-
ing substrate, approximatinga wide range of computing problems by propa-
gationthroughspaceandmorphologicaladaptation;includingpathplanning,
proximitygraphformationandminimisation,convexhulls,concavehulls,in-
ternalskeletons,Voronoidiagrams,combinatorialoptimisation(bothfeedback
VIII Preface
controlledandsimple‘blind’methods),datasmoothing,splinecurves,simple
geometricandnumericalstatisticalanalysis,andnoisyestimation.
Themodelcanalsoreproducethespontaneousandself-organisedemergence
ofoscillationswithin the ‘material’ofwhichit is composed.We demonstrate
howtheemergenceandsynchronisationofoscillationswithintheplasmodium
canalsooccurinthemodel,replicatingthetypicaloscillationpatternsofthe
Physarum plasmodium.We subsequentlydemonstratehowthese oscillations
canbeharnessedtogeneratedistributedcollectivetransportmechanismsand
controllablesoft-bodiedamoeboidmovement.
Weconcludebyconsideringhowthemulti-agentapproachcanbeextended
beyond modelling Physarum into the realm of modelling other dynamical
systemsincluding repulsiveparticles,mixedmaterialtypes,phaseseparation
phenomena, patterning in growing materials, phyllotaxis-type patterns and
3D transport networks.
The results in this book demonstrate the power and range of harness-
ing emergentphenomena arisingin simple multi-agentsystems for biological
modelling, computation and soft-robotics applications. With the aid of rich
explanatory images, video recordings and interactive simulations the reader
canexplorethepowerofbottom-upmulti-agentmodelsandwillhopefullybe
motivated to explore and develop their own experiments with these simple
yet powerful systems.
November 2014 Jeff Jones
Bristol
Acknowledgements
I would like to thank:
• Andrew Adamatzky for his experimental ‘wet’ slime mould contributions
to Chapters 9, 10, 12 and 13. And also for his designs of composite logic
gates that were implemented in Chapter 7. But most importantly for his
relentlesssupport,insights,motivationandencouragement.Afinermentor
could not be had.
• Soichiro Tsuda for his help and experimental contribution to Physarum
oscillatory synchronisation in Chapter 15.
• Larry Bull for his advice and positive support.
• Anumberofpeoplewhohavehelpedmeininformal,butconstructiveand
motivational,support,includingBenDeLacyCostello,SusanStepneyand
Yukio-Pegio Gunji.
• ThomasDitzinger,EngineeringEditorial,Springer-Verlag,forhis support
and help.
• Mr.GSenthilkumar,ECCProcessingTeam,ScientificPublishingServices,
for his proofing assistance.
I am grateful for the funding support of the University of the West of
England Department of Computer Science and the award of a UWE SPUR
Early Career Research grant, the award of Leverhulme Trust Research
Fellow, and the funding provided by the EU research programme FP7 ICT
Ref 316366.
Contents
Part I Slime Mould Physarum Polycephalum
1 Introduction and Overview............................ 3
1.1 Introduction and Overview ............................ 3
1.2 Objectives........................................... 4
1.3 Methodology......................................... 4
1.4 Structure of the Book................................. 5
2 Slime Mould Physarum Polycephalum ................. 13
2.1 Introduction ......................................... 13
2.2 Physarum Polycephalum............................... 13
2.3 Generation of Contractile Force and Oscillation Rhythm... 15
2.4 Formation and Adaptation of the Plasmodial
Tube Network........................................ 16
2.5 Oscillatory Synchronisation within the Plasmodium....... 17
2.6 Computational Behaviour of Physarum Polycephalum ..... 19
2.7 Physarum Polycephalum and Robotics .................. 23
2.8 Computational Models of Physarum Polycephalum ....... 25
2.9 Physarum as a Dynamical LALI Mechanism of Pattern
Formation ........................................... 27
2.10 Requisite Properties for Physarum Polycephalum
Models.............................................. 28
Part II Modelling Physarum Polycephalum
3 A Multi-agent Model of Physarum .................... 33
3.1 Introduction ......................................... 33
3.2 Motivations for Model Choice .......................... 33
3.3 A Multi-agent Virtual Material Approach ............... 35
3.4 Growth and Adaptation of the Virtual Plasmodium....... 40