Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies

674 Pages·2008·14.93 MB·English

by  Dario Floreano

COMpuTER SCIENCE/ARTIFICIAL INTELLIGENCE
Bio-Inspired M F Bio-Inspired
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Artificial Intelligence tt re
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THEORIES, METHODS, AND ss no Artificial Intelligence
TECHNOLOGIES i a
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Dario Floreano d

and Claudio Mattiussi
New approaches to artiÞcial intelligence spring from the idea Dario Floreano is Director of the Laboratory of Intelligent AB THEORIES, METHODS, AND
that intelligence emerges as much from cells, bodies, and so- Systems at the Swiss Federal Institute of Technology in
ri
cieties as it does from evolution, development, and learning. Lausanne (EPFL). He is the coauthor of Evolutionary Robotics: to TECHNOLOGIES
Traditionally, artiÞcial intelligence has been concerned with The Biology, Intelligence, and Technology of Self-Organizing if-
iI
reproducing the abilities of human brains; newer approaches Machines (MIT Press, 2000). Claudio Mattiussi is a researcher cn
take inspiration from a wider range of biological structures at the Laboratory of Intelligent Systems at EPFL. is
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that are capable of autonomous self-organization. Examples p
of these new approaches include evolutionary computation INTELLIGENT ROBOTICS AND AuTONOMOuS AGENTS SERIES l i
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and evolutionary electronics, artiÞcial neural networks, immune
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systems, biorobotics, and swarm intelligenceÑto mention ÒBio-Inspired ArtiÞcial Intelligence brings together all the things
td
only a few. This book offers a comprehensive introduction to IÕve been interested in for the last twenty-Þve years, and sur- e

the emerging Þeld of biologically inspired artiÞcial intelligence prises me by providing a coherent intellectual framework for l
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that can be used as an upper-level text or as a reference for them all. This book is a treasure trove of history from Darwin to i M
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researchers. Gibson and Walter, an unambiguous tutorial on how to build a d
e . D
Each chapter presents computational approaches inspired plethora of computational models, and a healthy exploration of
by a different biological system; each begins with background the philosophies that have driven wide-ranging research n alim
information about the biological system and then proceeds to agendas.Ó c #
develop computational models that make use of biological Rodney Brooks, Panasonic Professor of Robotics, e 974
7
concepts. The chapters cover evolutionary computation and Department of Electrical Engineering and Computer Science, MIT 4
3
electronics; cellular systems; neural systems, including neuro- 7
/1
morphic engineering; developmental systems; immune sys- ÒCompetent, lucid, well-written, Bio-Inspired ArtiÞcial Intelli- 6
/0
tems; behavioral systemsÑincluding several approaches to gence contains precisely the material you want from a com- 8
robotics, such as behavior-based, biomimetic, epigenetic, prehensive textbook, with many highly informative examples Or
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and evolutionary robots; and collective systems, including from biology, engineering, and computing. This book has the n
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swarm robotics as well as cooperative and competitive co- potential to become the new standard in the artiÞcial intelli- e G
evolving systems. Chapters end with a concluding overview gence Þeld.Ó r
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and suggested reading. Rolf Pfeifer, Director, ArtiÞcial Intelligence Laboratory, y D
University of Zurich k
.B
lu
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Dario Floreano
978-0-262-06271-8 and Claudio Mattiussi
THE MIT pRESS MASSACHuSETTS INSTITuTE OF TECHNOLOGY CAMBRIDGE, MASSACHuSETTS 02142 HTTp://MITpRESS.MIT.EDu
Bio-InspiredArtificialIntelligence
IntelligentRoboticsandAutonomousAgents
EditedbyRonaldC.Arkin
Dorigo,Marco,andMarcoColombetti,RobotShaping: AnExperimentinBe-
haviorEngineering
Arkin,RonaldC.,Behavior-BasedRobotics
Stone, Peter, Layered Learning in Multiagent Systems: A Winning Approach
toRoboticSoccer
Wooldridge,Michael,ReasoningaboutRationalAgents
Murphy,RobinR.,AnIntroductiontoAIRobotics
Mason,MatthewT.,MechanicsofRoboticManipulation
Kraus,Sarit,StrategicNegotiationinMultiagentEnvironments
Nolfi,Stefano,andDarioFloreano,EvolutionaryRobotics: TheBiology,Intelli-
gence,andTechnologyofSelf-OrganizingMachines
Siegwart, Roland, andIllahR.Nourbakhsh, IntroductiontoAutonomousMo-
bileRobots
Breazeal,CynthiaL.,DesigningSociableRobots
Bekey,GeorgeA.,AutonomousRobots: FromBiologicalInspirationtoImplemen-
tationandControl
Choset,Howie,KevinM.Lynch,SethHutchinson,GeorgeKantor,Wolfram
Burgard, Lydia E. Kavraki, and Sebastian Thrun, Principles of Robot Motion:
Theory,Algorithms,andImplementations
Thrun,Sebastian,WolframBurgard,andDieterFox,ProbabilisticRobotics
Mataric,MajaJ.,TheRoboticsPrimer
Wellman,MichaelP.,AmyGreenwald,andPeterStone,AutonomousBidding
Agents: StrategiesandLessonsfromtheTradingAgentCompetition
Floreano,Dario,andClaudioMattiussi,Bio-InspiredArtificialIntelligence:The-
ories,Methods,andTechnologies
Bio-Inspired Artificial Intelligence
Theories, Methods, and Technologies
DarioFloreano
ClaudioMattiussi
TheMITPress
Cambridge,Massachusetts
London,England
©2008MassachusettsInstituteofTechnology
All rights reserved. No part of this book may be reproduced in any form
byanyelectronicormechanicalmeans(includingphotocopying,recording,
orinformationstorageandretrieval)withoutpermissioninwritingfromthe
publisher.
MIT Press books may be purchased at special quantity discounts for busi-
nessorsalespromotionaluse. Forinformation,pleaseemailspecial_sales@
mitpress.mit.edu or write to Special Sales Department, The MIT Press, 55
HaywardStreet,Cambridge,MA02142.
ThisbookwassetbytheauthorsusingLYXandLATEX.
PrintedandboundintheUnitedStatesofAmerica.
LibraryofCongressCataloging-in-PublicationData
Floreano,Dario.
Bio-inspiredartificialintelligence: theories,methods,andtechnologies/
DarioFloreanoandClaudioMattiussi.
p. cm. –(Intelligentroboticsandautonomousagentsseries)
Includesbibliographicalreferencesandindex.
ISBN978-0-262-06271-8(hardcover: alk. paper)
1. Artificialintelligence–Dataprocessing. 2. Biologically-inspired com-
puting. 3. Self-organizingsystems. 4. Autonomousrobots. I.Mattiussi,
Claudio. II.Title.
Q336.F662008
006.3–dc22
2008008739
10 9 8 7 6 5 4 3 2 1
InmemoryofAnnamariaColloviniFloreano–D.F.
ToBetty–C.M.

Contents
Preface xi
Acknowledgments xiii
1 EvolutionarySystems 1
1.1 PillarsofEvolutionaryTheory 2
1.2 TheGenotype 5
1.3 ArtificialEvolution 13
1.4 GeneticRepresentations 16
1.5 InitialPopulation 21
1.6 FitnessFunctions 22
1.7 SelectionandReproduction 23
1.8 GeneticOperators 26
1.9 EvolutionaryMeasures 29
1.10 TypesofEvolutionaryAlgorithms 33
1.11 SchemaTheory 37
1.12 Human-CompetitiveEvolution 39
1.13 EvolutionaryElectronics 42
1.14 LessonsfromEvolutionaryElectronics 43
1.15 TheRoleofAbstraction 45
1.16 AnalogandDigitalCircuits 49
1.17 ExtrinsicandIntrinsicEvolution 53
1.18 DigitalDesign 58
1.19 EvolutionaryDigitalDesign 62
1.20 AnalogDesign 77
1.21 EvolutionaryAnalogDesign 79
1.22 MultipleObjectivesandConstraints 85
viii Contents
1.23 DesignVerification 90
1.24 ClosingRemarks 92
1.25 SuggestedReadings 97
2 CellularSystems 101
2.1 TheBasicIngredients 101
2.2 CellularAutomata 107
2.3 ModelingwithCellularSystems 110
2.4 SomeClassicCellularAutomata 118
2.5 OtherCellularSystems 124
2.6 Computation 134
2.7 ArtificialLife 138
2.8 ComplexSystems 145
2.9 AnalysisandSynthesisofCellularSystems 153
2.10 ClosingRemarks 159
2.11 SuggestedReadings 160
3 NeuralSystems 163
3.1 BiologicalNervousSystems 167
3.2 ArtificialNeuralNetworks 175
3.3 NeuronModels 177
3.4 Architecture 189
3.5 SignalEncoding 191
3.6 SynapticPlasticity 196
3.7 UnsupervisedLearning 198
3.8 SupervisedLearning 219
3.9 ReinforcementLearning 235
3.10 EvolutionofNeuralNetworks 238
3.11 NeuralHardware 250
3.12 HybridNeuralSystems 256
3.13 ClosingRemarks 261
3.14 SuggestedReadings 265
4 DevelopmentalSystems 269
4.1 PotentialAdvantagesofaDevelopmentalRepresentation 270
4.2 RewritingSystems 272
4.3 SynthesisofDevelopmentalSystems 296
4.4 EvolutionandDevelopment 298
4.5 DefiningArtificialEvolutionaryDevelopmentalSystems 299
Contents ix
4.6 EvolutionaryRewritingSystems 301
4.7 EvolutionaryDevelopmentalPrograms 310
4.8 EvolutionaryDevelopmentalProcesses 315
4.9 ClosingRemarks 332
4.10 SuggestedReadings 334
5 ImmuneSystems 335
5.1 HowBiologicalImmuneSystemsWork 337
5.2 TheConstituentsofBiologicalImmuneSystems 353
5.3 LessonsforArtificialImmuneSystems 366
5.4 AlgorithmsandApplications 373
5.5 ShapeSpace 375
5.6 NegativeSelectionAlgorithm 384
5.7 ClonalSelectionAlgorithm 388
5.8 Examples 390
5.9 ClosingRemarks 395
5.10 SuggestedReadings 396
6 BehavioralSystems 399
6.1 BehaviorinCognitiveScience 400
6.2 BehaviorinArtificialIntelligence 403
6.3 Behavior-BasedRobotics 407
6.4 BiologicalInspirationforRobots 419
6.5 RobotsasBiologicalModels 437
6.6 RobotLearning 449
6.7 EvolutionofBehavioralSystems 460
6.8 EvolutionandLearninginBehavioralSystems 482
6.9 EvolutionandNeuralDevelopmentinBehavioralSystems 494
6.10 CoevolutionofBodyandControl 499
6.11 TowardSelf-Reproduction 504
6.12 SimulationandReality 507
6.13 ClosingRemarks 511
6.14 SuggestedReadings 513
7 CollectiveSystems 515
7.1 BiologicalSelf-Organization 516
7.2 ParticleSwarmOptimization 524
7.3 AntColonyOptimization 527
7.4 SwarmRobotics 531