information driven self-organization of agents and agent collectives

176 Pages·2014·3.45 MB·English

INFORMATION DRIVEN SELF-ORGANIZATION
OF AGENTS AND AGENT COLLECTIVES
A thesis submitted in partial fulfilment of the
requirements of the University of Hertfordshire
of the degree of Doctor of Philosophy.
Malte Harder
May 2013
MalteHarder
AnsbacherStr.69a
28215Bremen
Germany
[email protected]
TypesetinCardov1.04andAdobeSourceSansProv1.038withConTEXt,TikZ,andPGFPlots.
LayoutinspiredbyTheElementsofTypographicStylebyRobertBringhurst.
Comicsfromhttp://www.xkcd.com(licensedunderCC-BY-NC2.5).
TypesetonThursdayApril3,2014.
©2013–2014MalteHarder.AllRightsReserved.
ABSTRACT
Fromavisualstandpointitisofteneasytopointoutwhetherasystemisconsideredtobe
self-organizingornot,thoughaquantitativeapproachwouldbemorehelpful.Information
theory,asintroducedbyShannon,providestherighttoolsnotonlyquantifyself-organ-
ization,butalsotoinvestigateitinrelationtotheinformationprocessingperformedby
individualagentswithinacollective.
Thisthesissetsouttointroducemethodstoquantifyspatialself-organizationincollective
systems in the continuous domain as a means to investigate morphogenetic processes.
In biology, morphogenesis denotes the development of shapes and form, for example
embryos, organs or limbs. Here, I will introduce methods to quantitatively investigate
shapeformationinstochasticparticlesystems.
In living organisms, self-organization, like the development of an embryo, is a guided
process,predeterminedbythegeneticcode,butexecutedinanautonomousdecentralized
fashion. Information is processed by the individual agents (e.g. cells) engaged in this
process.Hence,informationtheorycanbedeployedtostudysuchprocessesandconnect
self-organization and information processing. The existing concepts of observer based
self-organization and relevant information will be used to devise a framework for the
investigationofguidedspatialself-organization.
Furthermore,localinformationtransferplaysanimportantroleforprocessesofself-organ-
ization. In this context, the concept of synergy has been getting a lot attention lately.
Synergyisaformalizationoftheideathatforsomesystemsthewholeismorethanthesum
ofitspartsanditisassumedthatitplaysanimportantroleinself-organization,learningand
decisionmakingprocesses.Inthisthesis,anovelmeasureofsynergywillbeintroduced,
thataddressessomeofthetheoreticalproblemsthatearlierapproachesposed.
i
ii
»Themostexcitingphrasetohearinscience,theonethatheraldsnew
discoveries,isnot‘Eureka!’(Ifoundit!)but‘That’sfunny...’«
ISAACASIMOV,Unknown
»Thedifferencebetweenlifeandnon-lifeisamatternotofsubstancebut
ofinformation.«
RICHARDDAWKINS,TheGreatestShowonEarth
iii
iv
» To anyone who understands information theory and security and is in an infuri-
ating argument with someone who does not (possibly involving mixed case), I
sincerely apologize «
XKCD, 936
v
vi
ACKNOWLEDGEMENTS
I thank my principal supervisor Dr. Daniel Polani and my secondary supervisor Prof.
ChrystopherNehanivformanyinspiringandinvaluablediscussionswhichinarguablyhad
asignificantimpactonmyresearch.Iamalsothankingeverybodywhoprovidedfeedback
tothisthesisandthearticlesthatIpublishedduringthisprogramme.
Furthermore,IamgratefultomyformerhousematesfromGloucesterCourtforthemany
cupsofteawehad,thatwereoftenaccompaniedbyscientificdiscussions,tomyfriendsfor
offeringtheoccasionalescapefrominformationtheory,tomyfamilyforalwayssupporting
me,especiallyduringthelastmonthsofwritingthisthesis.Specialthanksgotomybrother
forkeepingmefromgoinginsaneonseveraloccasionsby‘showingmewherethatone
semicolonwasmissing’.
vii
CONTENTS
1 INTRODUCTION 15
Motivation 15
Overview 17
Contribution 18
2 BACKGROUND 20
InformationTheory 20
RelatedWork 28
3 QUANTIFYINGSELF-ORGANIZATION 37
Introduction 37
Self-Organization&Complexity 37
StatisticalComplexity 41
Self-organizationviaObservers 46
ComparisonofSC-OrganizationandO-Organization 51
EstimationofMulti-information 53
Discussion 62
4 SELF-ORGANIZATIONOFPARTICLESYSTEMS 68
ParticleCollectives&Self-organisation 69
Methods 74
Examples 76
Results 79
Discussion 88
5 INFOGENESIS 90
Introduction 90
Information-theoreticControlTheory 91
RelevantInformation 93
Embodiment&Perception-actionLoops 96
Multi-AgentRelevantInformation 99
RelevantInformation&Self-Organization 102
EpisodicTasks&ShapesasGoals 103
SharedControlandSensorCoordination 106
Experiments 108
Discussion 111
6 REDUNDANTINFORMATION 114
WhatisRedundancy 114
MeasureCandidates 115
ConstructionofaNewMeasure 119
PartialInformationDecomposition 125
viii